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Background: The insula is one of the most researched brain regions with many highly cited papers. However, unlike the literature of other fields, there is currently no study that has identified the 100 most cited papers within the literature of the insula. The aim of the current study was to fill in the knowledge gap by determining which publications concerning the insula have been cited most often, who contributed to them, and what topics they were dealing with. Methods: The Web of Science online database was searched to identify the 100 most cited publications mentioning the insular cortex in their titles, abstracts or keywords. To systematically exclude irrelevant publications, the search strategy was finalized as: TS = (insula OR insular OR "island of Reil") NOT TS = ("insular biogeography" OR "insular mammal*" OR "*insular lymphatic*") NOT WC = ("Geochemistry and Geophysics" OR "Ecology"). The identified publications were sorted in descending order of citation count. The 100 most cited publications concerning the insula of the brain were identified and their bibliometric data was extracted and assessed. The VOSviewer software was used with default parameters to generate a bubble map that analyzes and visualizes the words/phrases used in the titles and abstracts of the publications. Results: There were 67 articles on experiments/lab studies and 33 meta-analyses/reviews but no opinion or methods paper. They had an average of 943.4 citations (or 62.9 citations per year), 93.5 references and 13.4 pages. There were 35 papers published in open access. USA was the major contributing country. The most top-ranked publications were concerning emotion, salience and pain. Conclusion: Two-thirds of the publications concerned the normal brain function/mechanism (n = 67), whereas 20 publications concerned disease/therapeutic intervention and another 13 concerned normal anatomy. For the 67 original articles, 57 used human subjects whereas 10 used animal models. MRI was the commonest modality (n = 37), followed by PET (n = 16). Nine articles investigated by histology, two by multiple modalities and three by other modality.

Background Individuals with aphasia are often excluded from studies exploring poststroke cognition because so many of the standard cognitive assessments rely on language ability. Our primary objective was to examine the association between performance on cognitive tests and performance on comprehension and naming tests in poststroke aphasia. Second, we aimed to determine the association between language performance and a real-life measure of cognition (Kettle Test). Third, we explored the feasibility of administering cognitive tests in aphasia. Methods Thirty-six participants with poststroke aphasia and 32 controls were assessed on a battery of pen-and-paper cognitive tests recommended in stroke. Auditory comprehension was measured using the Comprehensive Aphasia Test and naming was measured using the Boston Naming Test. Twenty-two community dwelling participants with aphasia and controls were also asked to complete the Kettle Test. Multiple linear regressions were used to explore the relationship between language performance and performance on the cognitive tests. Feasibility was determined by quantifying missing data. Results The cognitive tests with the highest variance accounted for by auditory comprehension and naming were animal fluency (R2 = 0.67, R2 = 0.78) and the Hopkins Verbal Learning Test (recognition discrimination index) (R2 = 0.65, R2 = 0.78). All cognitive tests were significantly associated with auditory comprehension and naming, except for the Star Cancellation Test and the Kettle Test. Thirty-three percent of participants with aphasia were unable to complete all the cognitive tests. Conclusion Language and non-linguistic cognitive processes are often interrelated. Most pen-and-paper cognitive tests were significantly associated with both auditory comprehension and naming, even in tests that do not require a verbal response. Language performance was not significantly associated with a real-life cognitive performance measure. Task instructions, stimuli, and responses for completion need to be tailored for individuals with aphasia to minimize the influence of language deficits when testing non-linguistic cognitive performance.

It is well-known that geomagnetic fields have multiple components or parameters, and that these geomagnetic parameters are related to each other. In this paper, a parameter selection method is proposed, and this paper mainly discusses the correlation of geomagnetic field parameters for geomagnetic navigation technology. For the correlation analysis between geomagnetic parameters, the similarity calculation of the correlation coefficient is firstly introduced for geomagnetic navigation technology, and the grouped results are obtained by data analysis. At the same time, the search algorithm (Hex-path algorithm) is used to verify the correlation analysis results. The results show the same convergent state for the approximate correlation coefficient. In other words, the simulation results are in agreement with the similarity calculation results.

Cerebral energy supply is determined by the energy content of the blood. Accordingly, the brain is undersupplied during hypoglycaemia. Whether or not there is an additional cerebral energy demand that depends upon the energy content of the brain is considered differently in two opposing theoretical approaches. The Selfish-Brain theory postulates that the brain actively demands energy from the body when needed, while long-held theories, the gluco-lipostatic theory and its variants, deny such active brain involvement and view the brain as purely passively supplied. Here we put the competing theories to the test. We conducted a systematic review of a condition in which the rival theories make opposite predictions, i.e., experimental T1DM. The Selfish-Brain theory predicts that induction of experimental type 1 diabetes causes minor mass (energy) changes in the brain as opposed to major glucose changes in the blood. This prediction becomes our hypothesis to be tested here. A total of 608 works were screened by title and abstract, and 64 were analysed in full text. According to strict selection criteria defined in our PROSPERO preannouncement and complying with PRISMA guidelines, 18 studies met all inclusion criteria. Thirteen studies provided sufficient data to test our hypothesis. The 13 evaluable studies (15 experiments) showed that the diabetic groups had blood glucose concentrations that differed from controls by +294 ± 96% (mean ± standard deviation) and brain mass (energy) that differed from controls by −4 ± 13%, such that blood changes were an order of magnitude greater than brain changes (T = 11.5, df = 14, p < 0.001). This finding confirms not only our hypothesis but also the prediction of the Selfish-Brain theory, while the predictions of the gluco-lipostatic theory and its variants were violated. The current paper completes a three-part series of systematic reviews, the two previous papers deal with a distal and a proximal bottleneck in the cerebral brain supply, i.e., caloric restriction and cerebral artery occlusion. All three papers demonstrate that accurate predictions are only possible if one regards the brain as an organ that regulates its energy concentrations independently and occupies a primary position in a hierarchically organised energy metabolism.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=156816, PROSPERO, identifier: CRD42020156816.

A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model for improving the generalizability properties of an SNN., Comment: 23 pages, submitted to Frontiers in Neuroscience. arXiv admin note: text overlap with arXiv:2010.08195, arXiv:2006.09313 by other authors

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new object detector, named CRB-Net, is proposed to serve as a baseline. In experiments, CRB-Net was compared with 16 state-of-the-art object detection methods and outperformed all of them in terms of detection precision. In this paper, we further discuss the effect of the dataset diversity (e.g., instance size, lighting conditions), training set size, and dataset details (e.g., method of categorization). Cross-dataset validation shows that WSODD significantly outperforms other relevant datasets and that the adaptability of CRB-Net is excellent.

Learning Styles theory promises improved academic performance based on the identification of a personal, sensory preference for informational processing. This promise is not supported by evidence, and is in contrast to our current understanding of the neuroscience of learning. Despite this lack of evidence, prior research shows that that belief in the Learning Styles “neuromyth” remains high amongst educators of all levels, around the world. This perspective article is a follow up on prior research aimed at understanding why belief in the neuromyth of Learning Styles remains so high. We evaluated current research papers from the field of health professions education, to characterize the perspective that an educator would be given, should they search for evidence on Learning Styles. As in earlier research on Higher Education, we found that the use of Learning Style frameworks persist in education research for the health professions; 91% of 112 recent research papers published on Learning Styles are based upon the premise that Learning Styles are a useful approach to education. This is in sharp contrast to the fundamental principle of evidence-based practice within these professions. Thus any educator who sought out the research evidence on Learning Styles would be given a consistent but inaccurate endorsement of the value of a teaching technique that is not evidence based, possibly then propagating the belief in Learning Styles. Here we offer perspectives from both research and student about this apparent mismatch between educational practice and clinical practice, along with recommendations and considerations for the future.

Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. In this paper we describe a backpropagation-less learning approach to train a network of spiking GT neurons by enforcing sparsity constraints on the overall network spiking activity. The key features of the model and the proposed learning framework are: (a) spike responses are generated as a result of constraint violation and hence can be viewed as Lagrangian parameters; (b) the optimal parameters for a given task can be learned using neurally relevant local learning rules and in an online manner; (c) the network optimizes itself to encode the solution with as few spikes as possible (sparsity); (d) the network optimizes itself to operate at a solution with the maximum dynamic range and away from saturation; and (e) the framework is flexible enough to incorporate additional structural and connectivity constraints on the network. As a result, the proposed formulation is attractive for designing neuromorphic tinyML systems that are constrained in energy, resources, and network structure. In this paper, we show how the approach could be used for unsupervised and supervised learning such that minimizing a training error is equivalent to minimizing the overall spiking activity across the network. We then build on this framework to implement three different multi-layer spiking network architectures with progressively increasing flexibility in training and consequently, sparsity. We demonstrate the applicability of the proposed algorithm for resource-efficient learning using a publicly available machine olfaction dataset with unique challenges like sensor drift and a wide range of stimulus concentrations. In all of these case studies we show that a GT network trained using the proposed learning approach is able to minimize the network-level spiking activity while producing classification accuracy that are comparable to standard approaches on the same dataset.

This work joins a series that methodically tests the predictions of the Selfish-Brain theory. The theory postulates a vital ability of the mammalian brain, namely to give priority to its own energy metabolism. The brain behaves “selfishly” in this respect. For the cerebral artery occlusion studied here, the theory predicts an increase in blood glucose concentration, what becomes the hypothesis to be tested. We conducted a systematic review of cerebral-artery-occlusion papers to test whether or not the included studies could confirm this hypothesis. We identified 239 records, screened 231 works by title or abstract, and analyzed 89 by full text. According to strict selection criteria (set out in our PROSPERO preregistration, complying with PRISMA guidelines), 7 papers provided enough information to decide on the hypothesis. Our hypothesis could be fully confirmed for the 3 to 24 h after the onset of a transient 2 h or permanent occlusion. As for the mechanism, the theory predicts that the energy-deprived brain suppresses insulin secretion via the sympathoadrenal system, thereby preventing insulin-mediated glucose uptake into muscle and fat and, as a result, enhancing insulin-independent glucose uptake via the blood-brain barrier. Evidence from our included studies actually demonstrated cerebral insulin suppression. In all, the current work confirms the second major prediction of the Selfish-Brain theory that relates to a proximal bottleneck of the cerebral supply chain, cerebral artery occlusion. Its first major prediction relates to a distal supply bottleneck, caloric restriction, and is fulfilled as shown by our previous work, whereas the prediction of the long held gluco-lipostatic theory, which sees the brain as only passively supplied, is violated (Sprengell et al., 2021). The crucial point was that caloric restriction elicits smaller changes in mass (energy) in the brain than in the body. Taken together, the evidence from the current and previous work clearly shows that the most accurate predictions are possible with a theory that views the brain as an independently self-regulating energy compartment occupying a primary position in energy metabolism.

Understanding human hand movement functionality is fundamental in neuroscience, robotics, prosthetics, and rehabilitation. People are used to investigate movement functionality separately from qualitative or quantitative perspectives. However, it is still limited to providing an integral framework from both perspectives in a logical manner. In this paper, we provide a systematic framework to qualitatively classify hand movement functionality, build prehensile taxonomy to explore the general influence factors of human prehension, and accordingly design a behavioral experiment to quantitatively understand the hand grasp. In qualitative analysis, two facts are explicitly proposed: (1) the arm and wrist make a vital contribution to hand movement functionality; (2) the relative position (relative position in this paper is defined as the distance between the center of the human wrist and the object center of gravity) is a general influence factor significantly impacting human prehension. In quantitative analysis, the significant influence of three factors, object shape, size, and relative position, is quantitatively demonstrated. Simultaneously considering the impact of relative position, object shape, and size, the prehensile taxonomy and behavioral experiment results presented here should be more representative and complete to understand human grasp functionality. The systematic framework presented here is general and applicable to other body parts, such as wrist, arm, etc. Finally, many potential applications and the limitations are clarified.

This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.

At the end of 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in China, causing a new coronavirus disease, termed COVID-19 by the WHO on February 11, 2020. At the time of this paper (January 31, 2021), more than 100 million cases have been recorded, which have claimed over 2 million lives worldwide. The most important clinical presentation of COVID-19 is severe pneumonia; however, many patients present various neurological symptoms, ranging from loss of olfaction, nausea, dizziness, and headache to encephalopathy and stroke, with a high prevalence of inflammatory central nervous system (CNS) syndromes. SARS-CoV-2 may also target the respiratory center in the brainstem and cause silent hypoxemia. However, the neurotropic mechanism(s) by which SARS-CoV-2 affects the CNS remain(s) unclear. In this paper, we first address the involvement of astrocytes in COVID-19 and then elucidate the present knowledge on SARS-CoV-2 as a neurotropic virus as well as several other neurotropic flaviviruses (with a particular emphasis on the West Nile virus, tick-borne encephalitis virus, and Zika virus) to highlight the neurotropic mechanisms that target astroglial cells in the CNS. These key homeostasis-providing cells in the CNS exhibit many functions that act as a favorable milieu for virus replication and possibly a favorable environment for SARS-CoV-2 as well. The role of astrocytes in COVID-19 pathology, related to aging and neurodegenerative disorders, and environmental factors, is discussed. Understanding these mechanisms is key to better understanding the pathophysiology of COVID-19 and for developing new strategies to mitigate the neurotropic manifestations of COVID-19.

In this paper, we analyze and report on observable trends in human-human dyads performing collaborative manipulation (co-manipulation) tasks with an extended object (object with significant length). We present a detailed analysis relating trends in interaction forces and torques with other metrics and propose that these trends could provide a way of improving communication and efficiency for human-robot dyads. We find that the motion of the co-manipulated object has a measurable oscillatory component. We confirm that haptic feedback alone represents a sufficient communication channel for co-manipulation tasks, however we find that the loss of visual and auditory channels has a significant effect on interaction torque and velocity. The main objective of this paper is to lay the essential groundwork in defining principles of co-manipulation between human dyads. We propose that these principles could enable effective and intuitive human-robot collaborative manipulation in future co-manipulation research.

The retina is a complex, but well-organized neural structure that converts optical radiation into neural signals that convey photic information to a wide variety of brain structures. The present paper is concerned with the neural circuits underlying phototransduction for the central pacemaker of the human circadian system. The proposed neural framework adheres to orthodox retinal neuroanatomy and neurophysiology. Several postulated mechanisms are also offered to account for the high threshold and for the subadditive response to polychromatic light exhibited by the human circadian phototransduction circuit. A companion paper, modeling circadian phototransduction: Quantitative predictions of psychophysical data, provides a computational model for predicting psychophysical data associated with nocturnal melatonin suppression while staying within the constraints of the neurophysiology and neuroanatomy offered here.

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

The human cerebral cortex is parcellated in hundreds of areas using neuroanatomy and imaging methods. Alternatively, cortical areas can be classified into few cortical types according to their degree of laminar differentiation. Cortical type analysis is based on the gradual and systematic variation of laminar features observed across the entire cerebral cortex in Nissl stained sections and has profound implications for understanding fundamental aspects of evolution, development, connections, function, and pathology of the cerebral cortex. In this protocol paper, we explain the general principles of cortical type analysis and provide tables with the fundamental features of laminar structure that are studied for this analysis. We apply cortical type analysis to the micrographs of the Atlas of the human cerebral cortex of von Economo and Koskinas and provide tables and maps with the areas of this Atlas and their corresponding cortical type. Finally, we correlate the cortical type maps with the T1w/T2w ratio from widely used reference magnetic resonance imaging scans. The analysis, tables and maps of the human cerebral cortex shown in this protocol paper can be used to predict patterns of connections between areas according to the principles of the Structural Model and determine their level in cortical hierarchies. Cortical types can also predict the spreading of abnormal proteins in neurodegenerative diseases to the level of cortical layers. In summary, cortical type analysis provides a theoretical and practical framework for directed studies of connectivity, synaptic plasticity, and selective vulnerability to neurologic and psychiatric diseases in the human neocortex., This work was supported by the National Institute of Mental Health (grant nos. R01 MH101209 and R01 MH118500). MG-C was the recipient of a Beatriz Galindo senior research position in the Faculty of Medicine at Universidad Autónoma de Madrid (BEAGAL18/00098) and of a Grant for ICD Projects for the Beatriz Galindo Program Researchers at Universidad Autónoma de Madrid (SI2/PBG/2020-00014)

Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.

Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.

Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences., Comment: This paper is accepted for Frontiers in Neuroscience

Deep learning methods have shown their great capability of extracting high-level features from image and have been used for effective medical imaging classification recently. However, training samples of medical images are restricted by the amount of patients as well as medical ethics issues, making it hard to train the neural networks. In this paper, we propose a novel end-to-end three-dimensional (3D) attention-based residual neural network (ResNet) architecture to classify different subtypes of subcortical vascular cognitive impairment (SVCI) with single-shot T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in the diagnosis and early treatment of the different subtypes of SVCI. The experiment data in this paper are collected from 242 patients from the Neurology Department of Renji Hospital, including 78 amnestic mild cognitive impairment (a-MCI), 70 nonamnestic MCI (na-MCI), and 94 no cognitive impairment (NCI). The accuracy of our proposed model has reached 98.6% on a training set and 97.3% on a validation set. The test accuracy on an untrained testing set reaches 93.8% with robustness. Our proposed method can provide a convenient and effective way to assist doctors in the diagnosis and early treatment.

In cross-cultural communication and adjunct disciplines such as cross-cultural management and international business, there is a negativity bias of seeing cultural differences as a source of potential issues. The emergence of Positive Organizational Scholarship (POS) questions this problem-focused approach. This paper contributes to the ongoing discussion from neuroscience’s perspectives in several ways. Firstly, it provides a neurological look at this bias. Secondly, it proposes that that the problem-focused approach may (1) give us a biased outlook of cross-cultural encounters rather than a reality, (2) hinder creativity, (3) lead to the rebound effect, and (4) turn belief into reality. Finally, based on insight from neuroscience and adopting the POS lens with the connection between POS and creativity, it’s recommended that future research takes three directions: (1) Using similarity as the starting point; (2) strategize body language, context and theories; and (3) develop a multicultural mind. In essence, the paper contributes to existing knowledge of the field by employing an interdisciplinary approach, aiming to gain a more holistic view, provoke thoughts, and trigger future empirical studies.

3 For achieving motor recovery in individuals with sensorimotor deficits, augmented activation of 4 the appropriate sensorimotor system and facilitated induction of neural plasticity are essential. 5 An emerging procedure that combines peripheral nerve stimulation and its associative stimulation 6 with central brain stimulation is known to enhance the excitability of the motor cortex. In order to 7 effectively apply this paired stimulation technique, timing between central and peripheral stimuli 8 must be individually adjusted. There is a small range of effective timings between two stimuli, or 9 the inter-stimulus interval window (ISI-W). Properties of ISI-W from neuromodulation in response 10 to mechanical stimulation (Mstim) of muscles have been understudied because of the absence of 11 a versatile and reliable mechanical stimulator. This paper adopted a combination of transcranial 12 magnetic stimulation (TMS) and Mstim by using a high-precision robotic mechanical stimulator. 13 A pneumatically operated robotic tendon tapping device was applied. A low-friction linear cylinder 14 achieved high stimulation precision in time and low electromagnetic artifacts in physiological 15 measurements. This paper describes a procedure to effectively estimate an individual ISI-W from 16 the transiently enhanced motor evoked potential (MEP) with a reduced number of paired Mstim 17 and sub-threshold TMS trials by applying statistical sampling and regression technique. This paper 18 applied a total of four parametric and nonparametric statistical regression methods for ISI-W 19 estimation. The developed procedure helps to reduce time for individually adjusting effective ISI, 20 reducing physical burden on the subject.

Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based cerebrovascular segmentation methods have shown to yield outstanding performance, they are limited by their dependence on huge training dataset. In this paper, we propose an unsupervised cerebrovascular segmentation method of TOF-MRA images based on DNN and hidden Markov random field (HMRF) model. Our DNN-based cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested using 100 TOF-MRA images. The results were evaluated using the dice similarity coefficient (DSC), which reached a value of 0.79. The trained model achieved better performance than that of the traditional HMRF-based cerebrovascular segmentation method in binary pixel-classification. This paper combines the advantages of both DNN and HMRF to train the model with a not so large amount of the annotations in deep learning, which leads to a more effective cerebrovascular segmentation method.

Alzheimer's disease, and dementia, represent a common cause of disability and one of the most relevant challenges in the health world. In addition, these conditions do not have, at moment, a pharmacological treatment that can stop the pathological progress. Mild cognitive impairment (MCI), defined as the borderline between normal aging and early dementia, represents a meaningful field of study because, in the transition to dementia, clinicians have defined a useful therapeutic window. Additionally, due to the lack of effective pharmacological interventions, recent years have seen an increase in research into new technological solutions to assess, stimulate, and assist patients afflicted with Alzheimer's disease. This review aims to outline the use of information and communication technologies in the field studying MCI. Particularly, the goal is to depict the framework and describe the most worthwhile research efforts, in order to display the current technologies available, describe the research objectives, and delineate prospective future researches. Regarding data sources, the research was conducted within three databases, PubMed Central, Web of Science, and Scopus, between January 2009 and December 2017. A total of 646 articles were found in the initial search. Accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers to use for the study. Finally, 56 papers were fully evaluated and included in this review. Three major clinical application areas have been portrayed, namely “Cognitive Assessment,” “Treatment,” and “Assistance.” These have been combined with three main technological solutions, specifically “Sensors,” “Personal Devices,” and “Robots.” Furthermore, the study of the publications time series illustrates a steadily increasing trend, characterized by the enrollment of small groups of subjects, and particularly oriented to the subjects assistance using robots companion. In conclusion, despite the new technological solutions for people with MCI have received much interest, particularly regarding robots for assistance, nowadays it still owns vast room for improvement.

Studying and understanding human brain structures and functions have become one of the most challenging issues in neuroscience today. However, the mammalian nervous system is made up of hundreds of millions of neurons and billions of synapses. This complexity made it impossible to reconstruct such a huge nervous system in the laboratory. So, most researchers focus on C. elegans neural network. The C. elegans neural network is the only biological neural network that is fully mapped. This nervous system is the simplest neural network that exists. However, many fundamental behaviors like movement emerge from this basic network. These features made C. elegans a convenient case to study the nervous systems. Many studies try to propose a network formation model for C. elegans neural network. However, these studies could not meet all characteristics of C. elegans neural network, such as significant factors that play a role in the formation of C. elegans neural network. Thus, new models are needed to be proposed in order to explain all aspects of C. elegans neural network. In this paper, a new model based on game theory is proposed in order to understand the factors affecting the formation of nervous systems, which meet the C. elegans frontal neural network characteristics. In this model, neurons are considered to be agents. The strategy for each neuron includes either making or removing links to other neurons. After choosing the basic network, the utility function is built using structural and functional factors. In order to find the coefficients for each of these factors, linear programming is used. Finally, the output network is compared with C. elegans frontal neural network and previous models. The results implicate that the game-theoretical model proposed in this paper can better predict the influencing factors in the formation of C. elegans neural network compared to previous models.

The mass-spring system-like behavior is a powerful analysis tool to simplify human running/locomotion and is also known as the Spring Loaded Inverted Pendulum (SLIP) model. Beyond being just an analysis tool, the SLIP model is utilized as a template for implementing human-like locomotion by using the articulated robot. Since the dynamics of the articulated robot exhibits complicated behavior when projected into the operational space of the SLIP template, various considerations are required, from the robot's mechanical design to its control and analysis. Hence, the required technologies are the realization of pure mass-spring behavior during the interaction with the ground and the robust position control capability in the operational space of the robot. This paper develops a robot leg driven by the Series Elastic Actuator (SEA), which is a suitable actuator system for interacting with the environment, such as the ground. A robust hybrid control method is developed for the SEA-driven robot leg to achieve the required technologies. Furthermore, the developed robot leg has biarticular coordination, which is a human-inspired design that can effectively transmit the actuator torque to the operational space. This paper also employs Rotating Workspace (RW), which specializes in the control of the biarticulated robots. Various experiments are conducted to verify the performance of the developed robot leg with the control methodology.

Visual attention has an undeniable impact on our perception. Human brain is unable to process all of the information it faces with in a scene so, the most salient part of it will be chosen to process. This happens involuntary immediately after looking at a scene. It is commonly believed that neural activity in early visual areas induce a part of the scene salient than the others. Various researches since many years ago have shown that our neurons in early visual processing areas, from retina to LGN and then primary visual cortex are selective to some specific features. Everything we see and understand from the visual scene is based on these features and then the combination of them in higher visual areas. Different experiments are designed till now to investigate the impact of these features on saliency and understanding the mechanism behind this using different methods. The focus of this paper is on psychophysical tasks that are designed by researchers to study the role of early visual features in understanding saliency. Although most of them follow the same pattern to create saliency in their designed task, creativity and novelty is used in some tasks which leaded to interesting results. Various tasks designed to answer different questions related to low-level feature contrast are reviewed in this paper. Important and open questions also are discussed. One might extend this to investigate the impact of higher-level features in saliency in complex scenes or natural images.

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed.

Asynchrony among synaptic inputs may prevent a neuron from responding to behaviorally relevant sensory stimuli. For example, "octopus cells" are monaural neurons in the auditory brainstem of mammals that receive input from auditory nerve fibers (ANFs) representing a broad band of sound frequencies. Octopus cells are known to respond with finely timed action potentials at the onset of sounds despite the fact that due to the traveling wave delay in the cochlea, synaptic input from the auditory nerve is temporally diffuse. This paper provides a proof of principle that the octopus cells' dendritic delay may provide compensation for this input asynchrony, and that synaptic weights may be adjusted by a spike-timing dependent plasticity (STDP) learning rule. This paper used a leaky integrate and fire model of an octopus cell modified to include a "rate threshold," a property that is known to create the appropriate onset response in octopus cells. Repeated audio click stimuli were passed to a realistic auditory nerve model which provided the synaptic input to the octopus cell model. A genetic algorithm was used to find the parameters of the STDP learning rule that reproduced the microscopically observed synaptic connectivity. With these selected parameter values it was shown that the STDP learning rule was capable of adjusting the values of a large number of input synaptic weights, creating a configuration that compensated the traveling wave delay of the cochlea.

Increasing research efforts try to identify biological markers in order to support or eventually replace current practices of diagnosing mental disorders. Inasmuch as these disorders refer to subjective mental states, such efforts amount to their objectification. This gives rise to conceptual as well as empirical challenges: What kind of things are mental disorders? And how to deal with situations where subjective reports, clinical decisions, and brain scans contradict each other? The present paper starts out with a discussion of recent efforts to objectify beauty. Such attempts to quantify and localize psychological constructs in the brain are compared to earlier examples from the history of psychology. The paper then discusses personal and social implications of the objectification of subjective mental states, including mental disorders. The construct of Major Depressive Disorder, one of the most prevalent mental disorders, is then analyzed in more detail. It turns out that this is a very complex construct probably associated with highly heterogeneous actual instances of the disorder. It is then shown that it is unlikely to replace these symptoms' descriptions with patterns of brain activations, at least in the near future, given these patterns' empirical lack of specificity. The paper then discusses which of the disorder's core symptoms are more or less amenable to behavioral or neuroscientific investigation and analyses whether the heterophenomenological method can solve the problem. The conclusion is that the disorder construct is neither entirely subjective, nor completely objectifiable, and that clinical experts do well by continuing to take a pragmatical stance.

Various robotic exoskeletons have been proposed for hand function assistance during activities of daily living (ADL) of stroke survivors. However, traditional exoskeletons involve the use of complex rigid systems that impede the natural movement of joints, and thus reduce the wearability and cause discomfort to the user. The objective of this paper is to design and evaluate a soft robotic glove that is able to provide hand function assistance using fabric-reinforced soft pneumatic actuators. These actuators are made of silicone rubber which has an elastic modulus similar to human tissues. Thus, they are intrinsically soft and compliant. Upon air pressurization, they are able to support finger range of motion (ROM) and generate the desired actuation of the finger joints. In this work, the soft actuators were characterized in terms of their blocked tip force, normal and frictional grip force outputs. Combining the soft actuators and flexible textile materials, a soft robotic glove was developed for grasping assistance during ADL for stroke survivors. The glove was evaluated on five healthy participants for its assisted ROM and grip strength. Pilot test was performed in two stroke survivors to evaluate the efficacy of the glove in assisting functional grasping activities. Our results demonstrated that the actuators designed in this study could generate desired force output at a low air pressure. The glove had a high kinematic transparency and did not affect the active ROM of the finger joints when it was being worn by the participants. With the assistance of the glove, the participants were able to perform grasping actions with sufficient assisted ROM and grip strength, without any voluntary effort. Additionally, pilot test on stroke survivors demonstrated that the patient's grasping performance improved with the presence and assistance of the glove. Patient feedback questionnaires also showed high level of patient satisfaction and comfort. In conclusion, this paper has demonstrated the possibility of using soft wearable exoskeletons that are more wearable, lightweight, and suitable to be used on a daily basis for hand function assistance of stroke survivors during activities of daily living.

A previous paper from our lab (Shalom, 2009) presented evidence that the medial part of the prefrontal cortex is involved in the integration of raw, unintegrated information into coherent, wholistic mental representations such as perceptual objects, episodic memories, emotional states, and motor actions. It has used this analysis to classify some challenges encountered by people with Autism Spectrum Disorders, linking different types of difficulties in integration with different subareas of the medial prefrontal cortex. The current paper performs a similar analysis for the corresponding subareas of the lateral prefrontal cortex. It presents evidence that the lateral part of the prefrontal cortex is involved in the selection/inhibition of perceptual, memory, emotion, and motor aspects of processing. It then uses this analysis to classify challenges encountered by people with ADHD, linking different types of difficulties in selection/inhibition to different subareas of the lateral prefrontal cortex.

Background: Since the early 2010s, the neuroimaging field has paid more attention to the issue of false positives. Several journals have issued guidelines regarding statistical thresholds. Three papers have reported the statistical analysis of the thresholds used in fMRI literature, but they were published at least 3 years ago and surveyed papers published during 2007–2012. This study revisited this topic to evaluate the changes in this field. Methods: The PubMed database was searched to identify the task-based (not resting-state) fMRI papers published in 2017 and record their sample sizes, inferential methods (e.g., voxelwise or clusterwise), theoretical methods (e.g., parametric or non-parametric), significance level, cluster-defining primary threshold (CDT), volume of analysis (whole brain or region of interest) and software used. Results: The majority (95.6%) of the 388 analyzed articles reported statistics corrected for multiple comparisons. A large proportion (69.6%) of the 388 articles reported main results by clusterwise inference. The analyzed articles mostly used software Statistical Parametric Mapping (SPM), Analysis of Functional NeuroImages (AFNI), or FMRIB Software Library (FSL) to conduct statistical analysis. There were 70.9%, 37.6%, and 23.1% of SPM, AFNI, and FSL studies, respectively, that used a CDT of p ≤ 0.001. The statistical sample size across the articles ranged between 7 and 1,299 with a median of 33. Sample size did not significantly correlate with the level of statistical threshold. Conclusion: There were still around 53% (142/270) studies using clusterwise inference that chose a more liberal CDT than p = 0.001 (n = 121) or did not report their CDT (n = 21), down from around 61% reported by Woo et al. (2014). For FSL studies, it seemed that the CDT practice had no improvement since the survey by Woo et al. (2014). A few studies chose unconventional CDT such as p = 0.0125 or 0.004. Such practice might create an impression that the threshold alterations were attempted to show “desired” clusters. The median sample size used in the analyzed articles was similar to those reported in previous surveys. In conclusion, there seemed to be no change in the statistical practice compared to the early 2010s.

In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color). This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

This paper focusses on the simulation of the neural network of the Caenorhabditis elegans living organism, and more specifically in the modeling of the stimuli applied within behavioral experiments and the stimuli that is generated in the interaction of the C. elegans with the environment. To the best of our knowledge, all efforts regarding stimuli modeling for the C. elegansare focused on a single type of stimulus, which is usually tested with a limited subnetwork of the C. elegansneural system. In this paper, we follow a different approach where we model a wide-range of different stimuli, with more flexible neural network configurations and simulations in mind. Moreover, we focus on the stimuli sensation by different types of sensory organs or various sensory principles of the neurons. As part of this work, most common stimuli involved in behavioral assays have been modeled. It includes models for mechanical, thermal, chemical, electrical and light stimuli, and for proprioception-related self-sensed information exchange with the neural network. The developed models have been implemented and tested with the hardware-based Si elegans simulation platform.

This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications. Our position is that their combined use in computer-based therapies offers still unforeseen possibilities for the treatment of physical and mental disabilities. This is why, the paper expects that researchers and clinicians undergo a pathway toward human-avatar symbiosis for AVH by taking full advantage of new technologies. This outlook supposes to address challenging issues in the understanding of non-pharmacological treatment of schizophrenia-related disorders and the exploitation of VR/AR and BCI to achieve a real human-avatar symbiosis.

Molecular signal transmission in cell is very crucial for information exchange. How to understand its transmission mechanism has attracted many researchers. In this paper, we prove that signal transmission problem between neural tumor molecules and drug molecules can be achieved by synchronous control. To achieve our purpose, we derive the Fokker-Plank equation by using the Langevin equation and theory of random walk, this is a model which can express the concentration change of neural tumor molecules. Second, according to the biological character that vesicles in cell can be combined with cell membrane to release the cargo which plays a role of signal transmission, we preliminarily analyzed the mechanism of tumor-drug molecular interaction. Third, we propose the view of synchronous control which means the process of vesicle docking with their target membrane is a synchronization process, and we can achieve the precise treatment of disease by using synchronous control. We believe this synchronous control mechanism is reasonable and two examples are given to illustrate the correctness of our results obtained in this paper.

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

BCI systems that rely on EEG signals usually employ Fourier based methods for time-frequency decomposition and for subsequent feature extraction. The band-limited multiple Fourier linear combiner is well suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with co-variance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.

Background: Painful diabetic neuropathy (PDN) is a serious, polymorphic, and prevalent complication of diabetes mellitus. Most PDN treatment guidelines recommend a selection of drugs based on patient comorbidities. Despite the large numbers of medications available, most randomized clinical trials (RCTs) conducted so far have yielded unsatisfactory outcomes. Therefore, treatment may require a personalized approach based on pain phenotype or comorbidities. Methods: To evaluate whether or not a patient's pain phenotype or comorbidities can influence the response to a specific PDN treatment, we conducted a systematic review using two different approaches: pain phenotype and associated comorbidities-based treatment. Results: Out of 45 identified papers, 7 were thoroughly reviewed. We found four RCTs stratified according to pain phenotype with three main results: (1) paroxysmal pain had a better response to pregabalin (2) the preservation of thermal sensation or nociception anticipated a positive response to the topical treatment of pain and, (3) after a failure to duloxetine (60 mg/day), the patients with evoked pain or severe deep pain had a better response to association of duloxetine/pregabalin while those with paresthesia/dysesthesia benefited from duloxetine monotherapy (120 mg/day). By contrast, the other three papers provided weak and even contradictory evidence about PDN treatment based on comorbidities. Conclusion: Although more studies are needed to provide an adequate recommendation for clinical practice, our systematic review has provided some evidence that PDN phenotyping may optimize clinical outcomes and could, in the future, lead to both less empirical medicine and more personalized pain therapeutics. Univ Fed Sao Paulo UNIFESP, Diabet Ctr, Endocrinol Div, Escola Paulista Med, Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Brazilian Cochrane Ctr, Escola Paulista Med, Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Diabet Ctr, Endocrinol Div, Escola Paulista Med, Sao Paulo, Brazil Univ Fed Sao Paulo UNIFESP, Brazilian Cochrane Ctr, Escola Paulista Med, Sao Paulo, Brazil Web of Science

Raymond B. Cattell is credited with the development of the theory of fluid and crystallized intelligence. The genesis of this theory is, however, vague. Cattell, in different papers, stated that it was developed in 1940, 1941 or 1942. Carroll (1984, Multivariate Behavioral Research, 19, 300-306) noted the similarity of Cattell's theory to “Hebb's notion of two types of intelligence,” which was presented at the 1941 APA meeting, but the matter has been left at that. Correspondence between Cattell, Donald Hebb and George Humphrey of Queen's University, Kingston, Ontario, however, indicates that Cattell adopted Hebb's ideas of intelligence A and B and renamed them. This paper describes Hebb's two types of intelligence, and shows how Cattell used them to develop his ideas of crystallized and fluid intelligence. Hebb and Cattell exchanged a number of letters before Cattell's paper was rewritten in such a way that everyone was satisfied. This paper examines the work of Hebb and Cattell on intelligence, their correspondence, the development of the ideas of fluid and crystallized intelligence, and why Cattell (1943, p. 179) wrote that “Hebb has independently stated very clearly what constitutes two thirds of the present theory.”

This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization, and prediction emerge from the process of learning (Werbos, 1994, 2016a; National Science Foundation, 2008)? and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency (≥ 500 hz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data (Werbos, 1990)? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab (Fujisawa et al., 2008, 2015) which is consistent with a regular clock cycle of about 153.4 ms and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968-1974 (Werbos, 1994, 2006; Anderson and Rosenfeld, 1998). In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data (Box and Jenkins, 1970); in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large "continent" of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to "decoding" the neural code (Heller et al., 1995).

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation.

Last year we “celebrated” the 10th anniversary of the invention of the h-index (also known as the Hirsch factor; Hirsch, 2005), an indicator created by Jorge E. Hirsch, that attempts to measure the achievements of a research scientist. However, it not only appears that h-index has taken on a life of its own but also that the popularity of this formula currently surpasses the initial idea for its use envisioned by the inventor. Originally introduced as a simple characterization of the scientific output of a researcher (Hirsch, 2005), the h-index has come to be uncritically regarded as a “magic tool” that is applied to measure what is unmeasurable—the quality of science. As a result, it has become a “must have” indicator when applying for funds or a new position. Surprisingly, many decision-makers apparently are not fully aware of what it represents. According to its inventor, the h-index is the number of papers coauthored by the investigator with at least h citations each (Hirsch, 2005). That is, to be the proud owner of a high h-index, it is not sufficient to have authored many articles or for some of them to have been extensively cited. Rather, such an achievement must satisfy both issues: a considerable number of articles must be highly cited, which is reflected by ranking them according to the number of citations they have collected, and finding the one where the position on the list equals or is less than the number of the citations it garnered. The pros and cons of the h-index The initial idea of Hirsch was to discriminate the investigators who are persistently productive from those who experienced an isolated auspicious moment in their scientific life, and who currently only cut coupons from their popularity roll. Nevertheless, it assumes that researcher A, who published a breakthrough story that was extensively cited, should deserve less respect than researcher B who publishes often and regularly; however, the outcome of the latter's work has not contributed yet to any remarkable discovery. A good example is the inventor of the RNA isolation method, Piotr Chomczynski. He has in total over 65 000 citations to which he contributed almost exclusively (92.9% of all citations) with one single paper regarding the method he introduced in 1987 (Chomczynski and Sacchi, 1987). His current h-index is 23, relatively low for such a prodigious number of citations. Yet, could we imagine working with RNA over the past number of years without possessing this simple technique that is now the principle of virtually every commercial protocol related to RNA extraction? Notwithstanding, it might be argued—depreciating that discovery—that because this method is so simple, someone else would have been discovered it sooner or later. In response, let me cite my former mentor, professor Gunther Schutz, who would disprove similar arguments with a simple statement: If you say this is so trivial, why was it not you who discovered PCR? Breakthrough discoveries are not solely dependent on sophisticated science. Another problem with the h-index is the impossibility of comparing the investigators during different stages of their careers (even assuming comparisons among those representing the same field, which is another ambiguous factor). There is a certain correlation between the age of an investigator and h-index. Clearly and in any case, some of articles will accumulate citations and this number will increase over the time since they were first published. However, even the comparison between investigators at a similar career stage may often be misleading, particularly among young post-docs whose careers have just begun. We must be honest and acknowledge the fact that it is a rare occurrence that right after completing a PhD, such an investigator is able to independently decide about own career development. Mostly, the scientific achievements at this stage are primarily a derivative of the power of the PhD mentor and the reputation of the hosting institution. Another issue contributing to h-index limitations is that many research groups have different regulations regarding authorship. It is assumed that a researcher's name will be added to the authorship list only after considerable contribution has been made to the published work. However, what occurs fairly often is that being a “middle man” on the listing does not necessary reflect the significant contribution and, worth to be emphasized, the h-index does not differentiate between article authors who hold the most valuable first and last authorship position and those wherein the author's name appears as one among perhaps even 100 authors, as occurs with articles containing vast meta-analyses of clinical data. Although, some of these concerns were raised in the discussion part of the initial paper published by Hirsch (2005), they were overshadowed by the enormous popularity of this tool and its indiscriminate application. Despite of many critical yet unofficial discussions about the h-index, its limitations, and perhaps dangerous influence on science, the topic—if only tackled by publications—is usually narrowed to the pursuit of more and more sophisticated bibliometrics and proposals of new indicators (Bharathi, 2013; Biswal, 2013; Diaz et al., 2016; Wurtz and Schmidt, 2016). The critical voices seem to be less represented, nevertheless there are of course existing articles pointing out that past achievements of the scientists may not necessarily be correlated with future success and that all such rankings need context which means that the best method to gain an impression of the quality is still simply reading the papers (Wendl, 2007; von Bohlen Und Halbach, 2011). Perhaps the combination of looking into the context of the particular paper and the journal reputation gives the best assumption in this matter, however even this approach can be biased toward personal preferences and requires considerable amount of time. Last but not least, when examining the reasons why the h-index is not trustworthy, we should not neglect issues regarding fraud. Because the h-index does not discriminate self-citations, it is not difficult to predict that even the investigators who are poorly cited by others but who publish prodigiously, citing mostly themselves, will easily increase their h-index in the long run. Moreover, some evidence of misuse has been reported involving artificially pumped h-indices based on i.e., regular cross-citations between good friends (Kotov, 2010).

Important technological advances in the last decades paved the road to a great success story for electrically stimulating medical implants, including cochlear implants or implants for deep brain stimulation. However, there are still many challenges in reducing side effects and improving functionality and comfort for the patient. Two of the main challenges are the wish for smaller implants on one hand, and the demand for more stimulation channels on the other hand. But these two aims lead to a conflict of interests. This paper presents a novel design for an electrical feedthrough, the so called capacitive feedthrough, which allows both reducing the size, and increasing the number of included channels. Capacitive feedthroughs combine the functionality of a coupling capacitor and an electrical feedthrough within one and the same structure. The paper also discusses the progress and the challenges of the first produced demonstrators. The concept bears a high potential in improving current feedthrough technology, and could be applied on all kinds of electrical medical implants, even if its implementation might be challenging.

A large and convincing body of evidence demonstrates that medical conditions associated with chronic inflammatory and immunological abnormalities may represent risk factors for development of neuropsychiatric disorders. Indeed obesity, dysmetabolic conditions, including diabetes, metabolic syndrome, and autoimmune diseases are frequently associated to the development of neurodegenerative and psychiatric illnesses. Recent data have demonstrated that inflammatory cytokines and adipokines can interact with multiple transmitter pathways, neuroendocrine function, synaptic plasticity, and neurocircuits relevant to behavior regulation. Therefore, further understanding of mechanisms linking obesity, dysmetabolism, and (neuro)inflammation to neurological and mental alterations may be especially relevant to the treatment and prevention for such debilitating diseases that represent a major public health concern. Along this line, the current Research Topic provides appreciable evidence showing that activated inflammatory responses can undermine disease-relevant affective, emotional, social, and cognitive functions, so that inflammatory processes may be particularly relevant for the onset and progression of neuropsychiatric disorders. The present volume, at which contributed eminent scholars includes 11 review articles, 1 original research article, and 1 commentary and was organized in the attempt to cover the whole field of the interrelationship between inflammation, dysmetabolism, and neuropsychiatric disorders. To this regard, a very updated and comprehensive review article by Castanon et al. provided converging evidence showing that obesity might be associated with exacerbated neuroinflammation leading to dysfunction in vulnerable brain regions associated with mood regulation, learning, and memory (Castanon et al.). Their findings shed new light on the pathophysiological mechanisms responsible for brain disorders associated to obesity and suggest innovative strategies for the treatment of neuropsychiatric complications occurring in obese patients. Recent data, reviewed by Theoharides et al. demonstrated that pro-inflammatory molecules and related signaling pathways impact on multiple neurotransmitter circuits leading to synaptic damage, neuronal loss, and the activation of other inflammatory participants (Theoharides et al.). In particular, the authors emphasized the role of inflammation in the onset of brain “fog,” which represents a constellation of symptoms that include reduced mental acuity and cognition, inability to concentrate, as well as loss of short- and long-term memory. Brain “fog” may occur in patients suffering from many neuroimmune diseases, systemic mastocytosis, or disorders of mast cells activation, with increase in interleukin-6 (IL-6), tumor necrosis factor (TNF) levels. Moreover, the authors reported that the flavone luteolin might improve attention in children with autism and in mastocytosis patients with brain “fog” by reducing serum levels of TNF and IL-6. Similarly, Muller et al. provided an update about the role of inflammation in the pathogenesis of schizophrenia and the influence of immune activation on brain neurotransmissions. The authors also suggested an immune-based therapeutic approach in a subgroup of schizophrenics in whom an immune dysbalance is associated with chronic inflammation (Muller et al.). To this regard, Bonnot et al. emphasized the association between hidden neurometabolic diseases and psychiatric disorders. According to the authors, schizophrenia-like symptoms are by far the most frequent psychiatric warning signs of neurometabolic disorders. These morbidities have a significant impact on the life expectancy of patients, since subjects with chronic psychoses show a 2–3-fold increased risk of death, mostly from cardiovascular and metabolic diseases (Bonnot et al.). Therefore, Ventriglio et al. suggested that clinicians should screen and monitor these comorbidities to reduce mortality rates among patients with psychosis (Ventriglio et al.). Aging predisposes to diseases via several mechanisms that in part converge on inflammatory pathways. As age advances, the immune system undergoes a process of senescence accompanied by the increased production of inflammatory cytokines able to affect behavioral and cognitive performances. Deleidi et al. discussed the link between immune aging and neurological disorders describing how metabolic changes linked to the aging, immunosenescence, and inflammation can enhance the development of neurodegenerative and neuropsychiatric diseases. The authors argued that targeting age-associated (neuro)inflammation could delay the onset or slow disease progression (Deleidi et al.). In this context, glial cells have been proved to exert a crucial role. In fact, a growing body of evidence shows the remarkable complexity of inflammatory mechanisms in Alzheimer's disease (AD) and how they are often driven by alterations of glial cells functioning. In particular, the review paper by Steardo et al. explored the dual role of astrocytes in the onset and progression of AD. The authors questioning the validity of the prevalent neuron-centric vision of neuropsychiatric diseases highlighted the role of non-neuronal cells in the pathogenesis and progression of many diseases, also suggesting that astrocytes could be considered as promising target for future AD therapies (Steardo et al.). Recent human and preclinical studies provided convincing proof that AD is a degenerative disease associated with dysmetabolic aspects, including impairments in brain insulin responsiveness, glucose utilization, and energy metabolism (Barone et al., 2016). Given this background, the review paper by Bedse et al. focused on the role of insulin signaling in the deposition of neuritic plaques and intracellular neurofibrillary tangles (Bedse et al.). Moreover, the authors proposed intranasal insulin administration as potential therapeutics, since preliminary clinical trials have shown cognition improvement without significant side effects (Bedse et al.). Similarly, Lutz and Meyer discussed the potential role of the pancreatic peptide amylin as a link between metabolic and neurodegenerative disorders in general, and type 2 diabetes mellitus and AD in particular, emphasizing the possible influence of amylin in the amyloidogenetic process, and providing the rationale for an amylin-based strategy in the treatment of AD (Lutz and Meyer). However, although the link between dysmetabolism, obesity, inflammation, and neuropsychiatric/neurodegenerative disorders was extensively investigated in adulthood, there was no convincing literature demonstrating a positive correlation between maternal dysmetabolism and child's mental health. In this perspective, Rivera et al. summarized studies highlighting maternal obesity as one of the major risk factors for the development of mental health disorders in the offspring. Moving also from investigations carried out with animal models of maternal obesity, they further discussed the potential mechanisms by which such a condition impairs the development of neuronal circuitry, even suggesting possible strategies for prevention and therapeutic intervention (Rivera et al.). Besides environmental factors (e.g., diet), the epigenetic regulation of gene expression has emerged as a potential contributor to the susceptibility and development of obesity. To investigate the individual sensitivity to weight gain/resistance, the research paper by Cifani et al. studied the regulation of gene transcription of several hypothalamic neuropeptides involved in the control of energy balance in rats developing obesity or not, when fed with a high fat diet (Cifani et al.). The authors provided evidence of selective and time-dependent transcriptional regulation of target genes in obese rats compared to non-obese counterparts (Cifani et al.). Among the hypothalamic neuropeptides, oxytocin seems to play a crucial role in the regulation of feeding, metabolism, and in several behaviors associated with neuropsychiatric disorders (Romano et al., 2013a,b). To this regard, a very comprehensive review article by Romano et al. examined the role of oxytocin in a variety of physiological and behavioral processes suggesting that future research will clarify whether oxytocin may represent a promising target for innovative neuropsychiatric treatments (Romano et al.). Taken together, this special issue might provide scientists and clinicians with new insights into this emerging area of research and it might offer more cues for the development of promising therapeutic strategies against such devastating illnesses.

Throughout life, we learn to associate stimuli with their consequences. But some of the new information that we encounter forces us to abandon what we had previously acquired. This old information is then subject to a new learning process that is called extinction learning. This involves a large number of brain structures (Kattoor et al., 2013; Lissek et al., 2013, Lissek et al.; Merz et al., 2014). Extinction is an unusually complex learning process that can involve both Pavlovian (classical; Pavlov, 1927; Lattal and Lattal, 2012) and operant (instrument) conditioning (Skinner, 1938; Bouton et al., 2012). A further hallmark is its context-dependency (Bouton, 2004) that is likely to rely on a tight interaction between the hippocampus and other brain areas (e.g., Andre et al.; Icenhour et al., 2015). Thus, one of the aims of the present Research Topic was to incorporate studies that analyze the concert of neural structures that enable extinction learning. The old memory trace may be partly, or not at all forgotten during extinction (Ungor and Lachnit, 2006). It tends to re-emerge after a passage of time (spontaneous recovery), when re-exposure to the context of original learning occurs (renewal), or unexpected exposure to the unconditioned stimulus takes place (reinstatement). Such invasive memories are key symptoms of anxiety or pain disorders. They especially occur in individuals with enhanced susceptibility (Mosig et al.; Glombiewski et al., 2015). Although pathological fear in anxiety disorders can be treated through extinction-based approaches, treatment is not always successful in the long-term, underscoring the need to understand the mechanisms underlying impaired extinction. Therefore, the second aim of the Research Topic was to include publications that are situated at the transition between basic and clinical neuroscience. Given the relevance of extinction, it is astonishing how little we know about extinction learning, in terms of its neural fundaments and its development, especially when moving outside the realm of fear extinction in rodents. The third aim of the Research Topic was therefore to include papers on the uncharted territories of extinction learning that involve less-studied entities such as the immune system (Hadamitzky et al., 2016) or hormonal factors (Wolf et al., 2015; Maren and Holmes, 2016), less-studied species (Lengersdorf et al.) or novel paradigms (Wiescholleck et al., 2014). One specific goal of this Research Topic was to offer a basis for trans-species comparisons, as reflected by the spectrum of animals described that range from snails, through mice, rats, and pigeons. Several of the studies also describe extinction learning in humans, including pharmacological approaches. A number of studies (Andre et al.; Lengersdorf et al.; Andre and Manahan-Vaughan; Andrianov et al.; de Oliveira et al.; Lissek et al.) addressed neurotransmitter systems that are known to be involved in other forms of learning (Morris, 2013; Seyedabadi et al., 2014; Bauer, 2015) and in synaptic plasticity that is believed to underlie learning (Harley, 2004; Lesch and Waider, 2012; Park et al., 2013; Hansen and Manahan-Vaughan, 2014; Hagena et al., 2015). Here, for example, antagonism of N-methyl-D-aspartate receptors (NMDAR) prevented appetitive extinction in pigeons (Lengersdorf et al.), and GluN2B-containing NMDAR were found to play a key role in extinction of conditioned suppression of licking in rats (de Oliveira et al.). In an interesting corollary to the latter finding, Shumake and Monfils describe how conditioned suppression of licking is far more sensitive to extinction than freezing behavior, and along with Lee et al. investigated the impact of reactivating the original memory trace on extinction success. Examination of the role of dopamine receptors in appetitive learning in rats (Andre and Manahan-Vaughan) and predictive learning in humans (Lissek et al.), highlight differences that may relate to the species, or the extinction learning paradigm studied. Studies with regard to the neural basis of extinction learning, and its associated brain structures, revealed a specific and experience-dependent role of microcircuitry within the basolateral amygdala (Sangha). In their review article, Giustino and Maren challenge the common assumption that the medial prefrontal cortex (mPFC) mediates the expression, whereas the infralimbic cortex (IL) mediates the suppression of fear responses, whereas Lee at al. offer experimental evidence that extinction learning and retrieval trigger differentiated responses in the mPFC and amygdala. Goodman and Packard differentiated between extinction learning of response and place learning, and provide evidence that the effectivity of the extinction learning strategy depends on the memory system (dorsolateral striatum vs. hippocampus) that encoded the original experience. In line with studies in rats (Gershman et al.), Shiban et al. observed that gradually reducing the frequency of aversive stimuli, in a Pavlovian fear conditioning paradigm in humans, is more effective in averting the return of fear than abrupt stimulus withdrawal, and Zlomuzica et al. demonstrate that improved self-efficacy also improves fear extinction. By contrast, Vervliet and Indekkeu show that low-cost avoidance behavior is resilient to extinction. Earlier studies indicate that extinction learning is reinforced by a context change (Bouton, 2004). Here, Sjouwerman et al. report that the timing of the context change is decisive with regard to the functional outcome with regard to both extinction and renewal. At the structural and/or molecular levels, several studies provided evidence for the direct involvement of the hippocampus in extinction learning (Lissek et al.; de Oliveira et al.; Wille et al.). Whereas, de Oliveira et al. provide evidence of the involvement of the dorsal hippocampus in conditioned suppression, Wille et al. describe how modulation of the expression of chromatin remodeling factors in the ventral hippocampus rescue impaired extinction of conditioned fear. Several studies examined hormonal control of extinction learning in fear, or stress-based, paradigms (Perez-Torres et al.; Hadad-Ophir et al.; Labrenz et al.): aspects that were also addressed in a review article by Stockhorst and Antov and a research perspective by Elsenbruch and Wolf. What becomes apparent from these studies is the emergence of fine-tuning of our understanding as to which neural structures regulate extinction learning, what common denominators (and differences) exist between species, and how the regulation of extinction learning by neurotransmitter systems aligns with current knowledge as to the role of these systems in learning and memory. The papers compiled in this Research Topic offer new and valuable insights into the mechanisms and functional implementation of extinction learning at its different levels of complexity, and form the basis for new concepts and research ideas in this field.

The Editorial on the Research Topic Mechanisms of Innate Neuroprotection Two endogenous mechanisms of neuroprotection have been recently described: ischemic preconditioning and ischemic postconditioning. In fact, the concept of tolerance has been introduced long time ago and, remaining in the modern era of medicine, in 1927, the Austrian physician Julius Wagner-Juaregg was awarded the Nobel Prize in medicine for his research on pyrotherapy to treat psychiatric disorders. At that time, patients affected by dementia paralytica were preconditioned by the inoculation of malaria parasites (1). In the present Research Topic, newly characterized mechanisms involved in preconditioning and postconditioning neuroprotection will be discussed in order to provide tools to plan strategies which induce, mimic, or boost these endogenous protective responses. In particular, this Research Topic discusses the most important mechanisms involved in preconditioning and postconditioning, presenting a series of papers that provide up-to-date, state-of-the-art information on molecular and cellular mechanisms involved in the neuroprotective process elicited by these endogenous neuroprotectant strategies during brain ischemia and other neurological disorders, such as seizure and Alzheimer. In particular, these aspects are faced by tackling multiple angles of this complex phenomenon. In the first part of the book, three chapters are dedicated to the innate immune system and inflammation, giving special attention to the role of microglia (Fumagalli et al.), macrophages (Amantea et al.), and to some important modulators such as NF-kB (Lanzillotta et al.). The core of the Research Topic is dedicated to posttranscriptional modifications occurring during pre- and postconditioning. These aspects are discussed in seven chapters, including reviews (Saugstad; Jimenez-Mateos; Aune et al.), hypothesis and theory paper (Gidday), and original research articles (Reynolds et al.; Meller et al.; Flores-Rodriguez et al.), that highlight the importance of epigenetic modifications and their roles in mediating pre- and postconditioning neuroprotection. The role of non-coding RNA, with particular regards to microRNA, in innate neuroprotection is summarized in two up-to-date papers (Saugstad; Jimenez-Mateos). Experimental manipulation of miRNAs and/or their targets to induce pre- or post-stroke protection is also presented, as well as discussion on miRNA responses to current post-stroke therapies. Finally, in the last two chapters are described cellular mechanisms for neuroprotection, giving a special attention to those proteins involved in ionic homeostasis maintenance. In fact, although the mechanisms through which these two endogenous protective strategies exert their effects are not yet fully understood, recent evidence suggest that the maintenance of ionic homeostasis plays a key role in propagating these neuroprotective phenomena. In this last part of the book, it will be reviewed the role of plasmamembrane transporters and ionic channels involved in the control of ionic homeostasis and taking part to the neuroprotection induced by ischemic preconditioning and postconditioning, with particular regards to the Na+/Ca2+ exchangers (NCX), the plasma membrane Ca2+-ATPase (PMCA), the Na+/H+ exchange (NHE), the Na+/K+/2Cl− cotransport (NKCC), and the acid-sensing cation channels (ASICs) (Cuomo et al.). An interesting view from inside organelles is provided in the review by Sisalli et al. We hope that this Research Topic will stimulate the continuing efforts to understand the cell and physiological mechanisms underlying the origin of endogenous neuroprotective mechanisms. The understanding of the mechanisms involved in the neuroprotection elicited by preconditioning and postconditioning will be clinically relevant for it will contribute to discover new druggable targets in neurodegenerative disorder intervention. Therefore, the data presented here will highlight the capacity of ischemic preconditioning and postconditioning to be of benefit to patients.

In 2015, two papers using very different procedures presented strong evidence indicating that amnesia induced by protein synthesis inhibitor did not prevent memory formation, challenging the classical consolidation hypothesis (Gisquet-Verrier et al., 2015; Ryan et al., 2015). Both of them proposed that amnesia resulted from retrieval difficulties. In their recent review, Ryan and Tonegawa (2016) went further and proposed that retrieval difficulties were due to the absence of training-induced morphological changes induced by protein synthesis inhibition. However, as explained in the present comment, there is evidence arguing against this position. According to the traditional consolidation hypothesis which has dominated the literature concerning memory formation for more than 50 years, newly acquired information is progressively encoded into a stable long-term memory. Most of the support for this hypothesis relies on retrograde amnesia produced by various treatments delivered just after a training episode. Amnesic agents initially were severe treatments (e.g., electroconvulsive shock, cortical spreading depression, hypothermia…) that disrupted the normal functioning of the brain during this stabilization process. Later, protein synthesis inhibitors (PSI) became the most commonly used amnesic agents as they could be targeted directly into brain areas assumed critical for specific aspects of the memory, such as the amygdala for fear conditioning (Nader et al., 2000). The current version of the consolidation hypothesis now assumes that consolidation is achieved through processes requiring new protein synthesis. Historically, the consolidation hypothesis has been challenged by a series of studies showing that memory can be recovered following the exposure to a variety of “reminder” treatments (Lewis, 1979; Miller and Matzel, 2006). These studies were not consistent with a consolidation hypothesis postulating that interrupting the stabilization processes should lead to a permanent loss of the memory, and rather suggested that amnesia resulted from retrieval difficulties (Lewis, 1979). However, the finding of long-term potentiation, which fit well with the consolidation hypothesis, became the dominant explanation of memory consolidation at a cellular level (Abel and Lattal, 2001) and the behavioral criticisms were largely ignored. In a recent series of experiments Tonegawa and his colleagues (Ryan et al., 2015), using sophisticated techniques involving learning-dependent cell labeling, were able to show that a memory survives amnesia. They demonstrated that while a protein synthesis inhibitor delivered immediately after the conditioning episode induced amnesia; optogenetic activation of the engram cells unexpectedly resulted in memory retrieval. In other words, PSI rendered rats amnesic but the memory was “rescued” by a direct activation of the memory engram cells. This result demonstrated that protein synthesis was not required for the formation of new memory, challenging the consolidation hypothesis. In the same study, the authors demonstrated that inhibition of protein synthesis delivered within the brain also prevented enhanced dendritic spine density and synaptic strength normally observed in the engram cells engaged in the fear memory. Based on this finding, Tonegawa and colleagues proposed that the primary role of training-induced morphological changes is to participate in memory retrieval by allowing natural recall cues access to the engram cells. In their recent review paper, Ryan and Tonegawa (2016) returned to these results by placing a greater emphasis on the role protein-induced morphological changes may have in retrieval processes. However, their position seems inconsistent with two sets of results. It is well known that amnesia can be induced by treatments delivered both after conditioning and after memory reactivation (Lewis, 1979; Nader et al., 2000) and Tonegawa's team replicated this finding (Ryan et al., 2015). In the case of post-reactivation amnesia, the morphological changes thought to be induced by initial training normally took place after training, as rats demonstrated substantial levels of freezing during memory reactivation, indicating that retrieval processes were in place at that time. However, rats treated with PSI after reactivation were unable to express fear to the training context on the next day. Accordingly, either the morphological changes promoted by initial training were abolished by the post-reactivation PSI injection (which seems unlikely), or their difficulties in expressing fear arose from a source other than a lack of morphological changes. Another set of studies indicates that retrograde amnesia can be alleviated by pretest exposure to various reminder cues including the amnesic treatment itself (Lewis, 1979; Riccio et al., 2006). It has been shown that, re-exposure to the PSI agent can reverse amnesia resulting from either post-training or post-reactivation PSI administration, independently of whether the treatment was administered systemically or intracerebrally (Bradley and Galal, 1988; Briggs and Olson, 2013; Gisquet-Verrier et al., 2015). These results complement the findings from the Tonegawa lab in showing that the formation of new memory does not require de novo protein synthesis. and that retrograde amnesia results from a deficit of memory retrievability rather than disruption of consolidation. The issue raised here only concerns the origins of memory retrieval difficulties. For Tonegawa, these difficulties are due to the PSI preventing training induced morphological changes. However, evidence that amnesia can be reversed by reinstating the internal state induced by PSI shortly before the retention test reveals that retrieval can be achieved even when the morphological changes due to conditioning were prevented by PSI. We proposed an alternative retrieval interpretation to that of Ryan and Tonegawa based on a combination of memory malleability and state-dependency (Gisquet-Verrier et al., 2015). Due to the malleability of active memory (i.e., after training and reactivation), the internal state induced by PSI is integrated into the initial memory. Subsequent retrieval difficulties would thus be due to the absence of this salient cue at the time of testing, an effect which can be overcome by reinstating the internal state before the retention test. Based on the findings described above, de novo protein synthesis is neither required for memory formation nor retrieval and this leaves open the question concerning the role of the protein dependent morphological changes taking place shortly after training. More generally, it seems clear, as Ryan and Tonegawa (2016) have pointed out, that retrieval oriented interpretations of amnesia offer new views on memory, suggesting that it is now timely to revisit the consolidation hypothesis.