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Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs., Competing Interests: MG, NB No competing interests declared, (© 2023, Gillett and Brunel.)

Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learning principles. This model accounts for word segmentation tasks, one primary paradigm in statistical learning. In this discussion paper we review this model and compare it with the Hebbian model previously presented by Tovar and Westermann (T&W, 2017a; 2017b; 2018) that has accounted for serial reaction time tasks, cross-situational learning, and categorization paradigms, all relevant in the study of statistical learning. We discuss the similarities and differences between both models, and their key findings. From our analysis, we question the concept of "forgetting" in the model of E&J and their suggestion of considering forgetting as the critical ingredient for successful statistical learning. We instead suggest that a set of simple but well-balanced mechanisms including spreading activation, activation persistence, and synaptic weight decay, all based on biologically grounded principles, allow modeling statistical learning in Hebbian neural networks, as demonstrated in the T&W model which successfully covers learning of nonadjacent dependencies and accounts for differences between typical and atypical populations, both aspects that have not been fully demonstrated in the E&J model. We outline the main computational and theoretical differences between the E&J and T&W approaches, present new simulation results, and discuss implications for the development of a computational cognitive theory of statistical learning., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Long-term potentiation (LTP) is one of the most widely studied forms of neural plasticity, and is thought to be the principle mechanism underlying long-term memory and learning in the brain. Sensory paradigms utilising electroencephalography (EEG) and sensory stimulation to induce LTP have allowed translation from rodent and primate invasive research to non-invasive human investigations. This review focusses on visual sensory LTP induced using repetitive visual stimulation, resulting in changes in the visually evoked response recorded at the scalp with EEG. Across 15 years of use and replication in humans several major paradigm variants for eliciting visual LTP have emerged. The application of different paradigms, and the broad implementation of visual LTP across different populations combines to provide a rich and sensitive account of Hebbian LTP, and potentially non-Hebbian plasticity mechanisms. This review will conclude with a discussion of how these findings have advanced existing theories of perceptual learning by positioning Hebbian learning both alongside and within other major theories such as Predictive Coding and The Free Energy Principle., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In healthy women, fluctuations in hormones including progesterone and oestradiol lead to functional changes in the brain over the course of each menstrual cycle. Though considerable attention has been directed towards understanding changes in human cognition over the menstrual cycle, changes in underlying processes such as neural plasticity have largely only been studied in animals. In this study we explored predictive coding and repetition suppression via the roving mismatch negativity paradigm as a model of short-term plasticity (Garrido, Kilner, Kiebel, et al., 2009), and Hebbian learning via visual sensory long-term potentiation (LTP) as a model of long-term plasticity (Teyler et al., 2005). Electroencephalography (EEG) was recorded in 20 females during their early follicular and mid-luteal phases. Event-related potential (ERP) analyses were complemented with dynamic causal modelling (DCM) to characterise changes in the underlying neural architecture. More sustained variability in the ERP response to a change in tone during the luteal phase are interpreted as a delayed habituation of the P3a component in the luteal relative to the follicular phase. The additional increased forward connection strength over tone repetitions compared to the follicular phase suggests that, in this phase, females may be less efficient when processing deviations from predicted sensory input (error). In contrast, there appears to be no reliable change in sensory LTP. This suggests that predictive coding, but not Hebbian plasticity is modified in the mid-luteal compared to the follicular phase, at least at the days of the menstrual cycle tested. This finding implicates the human menstrual cycle in complex changes in neural plasticity and provides further evidence for the importance of considering the menstrual cycle when including females in electrophysiological research., (Copyright © 2018 Elsevier Inc. All rights reserved.)

One of the key statements of linguistic relativity is that language has a causal effect on perception. Although much previous research has addressed such putative language perception causality, no firm proof is available thus far which demonstrates that verbal labels help or otherwise influence perceptual processes. Here, we tested the hypothesis of language perception causality by using novel, minimally-different tactile-patterned stimuli applied to the finger, which initially could not be discriminated by our participants. By combining novel verbal pseudoword- and novel tactile-patterned stimuli in an implicit learning experiment, we show a language-induced facilitation in tactile-patterned stimulus discrimination. After one week of intensive yet implicit learning of tactile stimuli in the presence of irrelevant consistent verbal labels, participants demonstrated significant discrimination improvement. In contrast, the same participants showed no improvement in discriminating tactile-patterned stimuli that had been learnt in the context of variable linguistic stimuli. These results show that specific mental links between verbal labels and perceptual information brought about by their correlated presentation enable one to better discriminate said sensory information (and build percepts)., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity., (Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Although several experiments reported rapid cortical plasticity induced by passive exposure to novel segmental patterns, few studies have devoted attention to the neural dynamics during the rapid learning of novel tonal word-forms in tonal languages, such as Chinese. In the current study, native speakers of Mandarin Chinese were exposed to acoustically matched real and novel segment-tone patterns. By recording their Mismatch Negativity (MMN) responses (an ERP indicator of long-term memory traces for spoken words), we found enhanced MMNs to the novel word-forms over the left-hemispheric region in the late exposure phase relative to the early exposure phase. In contrast, no significant changes were identified in MMN responses to the real word during familiarisation. Our results suggest a rapid Hebbian learning mechanism in the human neocortex which develops long-term memory traces for a novel segment-tone pattern by establishing new associations between the segmental and tonal representations., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Memory cells, the ultimate neurobiological substrates of working memory, remain active for several seconds and are most commonly found in prefrontal cortex and higher multisensory areas. However, if correlated activity in "embodied" sensorimotor systems underlies the formation of memory traces, why should memory cells emerge in areas distant from their antecedent activations in sensorimotor areas, thus leading to "disembodiment" (movement away from sensorimotor systems) of memory mechanisms? We modelled the formation of memory circuits in six-area neurocomputational architectures, implementing motor and sensory primary, secondary and higher association areas in frontotemporal cortices along with known between-area neuroanatomical connections. Sensorimotor learning driven by Hebbian neuroplasticity led to formation of cell assemblies distributed across the different areas of the network. These action-perception circuits (APCs) ignited fully when stimulated, thus providing a neural basis for long-term memory (LTM) of sensorimotor information linked by learning. Subsequent to ignition, activity vanished rapidly from APC neurons in sensorimotor areas but persisted in those in multimodal prefrontal and temporal areas. Such persistent activity provides a mechanism for working memory for actions, perceptions and symbols, including short-term phonological and semantic storage. Cell assembly ignition and "disembodied" working memory retreat of activity to multimodal areas are documented in the neurocomputational models' activity dynamics, at the level of single cells, circuits, and cortical areas. Memory disembodiment is explained neuromechanistically by APC formation and structural neuroanatomical features of the model networks, especially the central role of multimodal prefrontal and temporal cortices in bridging between sensory and motor areas. These simulations answer the "where" question of cortical working memory in terms of distributed APCs and their inner structure, which is, in part, determined by neuroanatomical structure. As the neurocomputational model provides a mechanistic explanation of how memory-related "disembodied" neuronal activity emerges in "embodied" APCs, it may be key to solving aspects of the embodiment debate and eventually to a better understanding of cognitive brain functions., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

Transcranial magnetic stimulation (TMS) has shown promise as a treatment tool, with one FDA approved use. While TMS alone is able to up- (or down-) regulate a targeted neural system, we argue that TMS applied as an adjuvant is more effective for repetitive physical, behavioral and cognitive therapies, that is, therapies which are designed to alter the network properties of neural systems through Hebbian learning. We tested this hypothesis in the context of a slow motor learning paradigm. Healthy right-handed individuals were assigned to receive 5 Hz TMS (TMS group) or sham TMS (sham group) to the right primary motor cortex (M1) as they performed daily motor practice of a digit sequence task with their non-dominant hand for 4 weeks. Resting cerebral blood flow (CBF) was measured by H2(15)O PET at baseline and after 4 weeks of practice. Sequence performance was measured daily as the number of correct sequences performed, and modeled using a hyperbolic function. Sequence performance increased significantly at 4 weeks relative to baseline in both groups. The TMS group had a significant additional improvement in performance, specifically, in the rate of skill acquisition. In both groups, an improvement in sequence timing and transfer of skills to non-trained motor domains was also found. Compared to the sham group, the TMS group demonstrated increases in resting CBF specifically in regions known to mediate skill learning namely, the M1, cingulate cortex, putamen, hippocampus, and cerebellum. These results indicate that TMS applied concomitantly augments behavioral effects of motor practice, with corresponding neural plasticity in motor sequence learning network. These findings are the first demonstration of the behavioral and neural enhancing effects of TMS on slow motor practice and have direct application in neurorehabilitation where TMS could be applied in conjunction with physical therapy., (© 2013.)

A neurobiologically constrained model of semantic learning in the human brain was used to simulate the acquisition of concrete and abstract concepts, either with or without verbal labels. Concept acquisition and semantic learning were simulated using Hebbian learning mechanisms. We measured the network's category learning performance, defined as the extent to which it successfully (i) grouped partly overlapping perceptual instances into a single (abstract or concrete) conceptual representation, while (ii) still distinguishing representations for distinct concepts. Co-presence of linguistic labels with perceptual instances of a given concept generally improved the network's learning of categories, with a significantly larger beneficial effect for abstract than concrete concepts. These results offer a neurobiological explanation for causal effects of language structure on concept formation and on perceptuo-motor processing of instances of these concepts: supplying a verbal label during concept acquisition improves the cortical mechanisms by which experiences with objects and actions along with the learning of words lead to the formation of neuronal ensembles for specific concepts and meanings. Furthermore, the present results make a novel prediction, namely, that such 'Whorfian' effects should be modulated by the concreteness/abstractness of the semantic categories being acquired, with language labels supporting the learning of abstract concepts more than that of concrete ones. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'. [ABSTRACT FROM AUTHOR]

A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. Proc 33rd Conf Neural Inf Process Systems (NeurIPS 2020). 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvaged in artificial feed-forward networks by optimizing coefficients of classic Hebbian rules to dynamically control the networks' weights instead of optimizing the weights directly. Although such models fail to capture many important neurophysiological details, allying the fields of neuroscience and artificial intelligence in this way bears many fruits for both fields, especially when computational models engage with topics with a rich history in neuroscience such as Hebbian plasticity. [ABSTRACT FROM AUTHOR]

Background: Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions. Results: To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications — H3K36me3, H3K79me1, H3K79me2, and H4K8ac — that are associated with coding regions of active genes. Other marks — H4K12ac, H3K14ac, H3K27me3, and H2AK5ac — were found to be weakly associated with coding regions of inactive genes. Conclusions: This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code. [ABSTRACT FROM AUTHOR]

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed. [ABSTRACT FROM AUTHOR]

Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can store the essential, and then infer the details to match perception. The Willshaw Memory is a simple abstract model for cortical computations which implements mechanisms of biological memories. Using our recently proposed sparse coding prescription for visual patterns [34] , this model can store and retrieve an impressive amount of real-world data in a fault-tolerant manner. In this paper, we extend the capabilities of the basic Associative Memory Model by using a Multiple-Modality framework. In this setting, the memory stores several modalities (e.g., visual, or textual) of each pattern simultaneously. After training, the memory can be used to infer missing modalities when just a subset is perceived. Using a simple encoder-memory-decoder architecture, and a newly proposed iterative retrieval algorithm for the Willshaw Model, we perform experiments on the MNIST dataset. By storing both the images and labels as modalities, a single Memory can be used not only to retrieve and complete patterns but also to classify and generate new ones. We further discuss how this model could be used for other learning tasks, thus serving as a biologically-inspired framework for learning. [ABSTRACT FROM AUTHOR]

Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognized. From the perspective of what we might call "classical cognitive science" it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-)cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols. Recent work on memory has been adding fuel to the fire and current findings in neuroscience now provide first tentative neurobiological evidence for the cognitive scientists' doubts about the synapse as the (sole) locus of memory in the brain. This paper briefly considers the history and appeal of synaptic plasticity as a memory mechanism, followed by a summary of the cognitive scientists' objections regarding these assertions. Next, a variety of tentative neuroscientific evidence that appears to substantiate questioning the idea of the synapse as the locus of memory is presented. On this basis, a novel way of thinking about the role of synaptic plasticity in learning and memory is proposed. [ABSTRACT FROM AUTHOR]

Hebbian-type learning rules, which underlie learning and refinement of neuronal connectivity, postulate input specificity of synaptic changes. However, theoretical analyses have long appreciated that additional mechanisms, not restricted to activated synapses, are needed to counteract positive feedback imposed by Hebbian-type rules on synaptic weight changes and to achieve stable operation of learning systems. The biological basis of such mechanisms has remained elusive. Here we show that, in layer 2/3 pyramidal neurons from slices of visual cortex of rats, synaptic changes induced at individual synapses by spike timing-dependent plasticity do not strictly follow the input specificity rule. Spike timing-dependent plasticity is accompanied by changes in unpaired synapses: heterosynaptic plasticity. The direction of heterosynaptic changes is weight-dependent, with balanced potentiation and depression, so that the total synaptic input to a cell remains preserved despite potentiation or depression of individual synapses. Importantly, this form of heterosynaptic plasticity is induced at unpaired synapses by the same pattern of postsynaptic activity that induces homosynaptic changes at paired synapses. In computer simulations, we show that experimentally observed heterosynaptic plasticity can indeed serve the theoretically predicted role of robustly preventing runaway dynamics of synaptic weights and activity. Moreover, it endows model neurons and networks with essential computational features: enhancement of synaptic competition, facilitation of the development of specific intrinsic connectivity, and the ability for relearning. We conclude that heterosynaptic plasticity is an inherent property of plastic synapses, crucial for normal operation of learning systems. [ABSTRACT FROM AUTHOR]

A substantial number of works have aimed at modeling the receptive field properties of the primary visual cortex (V1). Their evaluation criterion is usually the similarity of the model response properties to the recorded responses from biological organisms. However, as several algorithms were able to demonstrate some degree of similarity to biological data based on the existing criteria, we focus on the robustness against loss of information in the form of occlusions as an additional constraint for better understanding the algorithmic level of early vision in the brain. We try to investigate the influence of competition mechanisms on the robustness. Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. Each of those methods is known to be capable of developing receptive fields comparable to those of V1 simple-cells. Since measuring the robustness of methods having simple-cell like receptive fields against occlusion is difficult, we measure the robustness using the classification accuracy on the MNIST hand written digit dataset. For this we trained all methods on the training set of the MNIST hand written digits dataset and tested them on a MNIST test set with different levels of occlusions. We observe that methods which employ competitive mechanisms have higher robustness against loss of information. Also the kind of the competition mechanisms plays an important role in robustness. Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule. [ABSTRACT FROM AUTHOR]

In the present work, we set out to assess whether and how much people learn in response to their stereotypic assumptions being confirmed, being disconfirmed, or remaining untested. In Study 1, participants made a series of judgments that could be influenced by stereotypes and received feedback that confirmed stereotypes the majority of the time, feedback that disconfirmed stereotypes the majority of the time, or no feedback on their judgments. Replicating past work on confirmation bias, patterns in the conditions with feedback indicated that pieces of stereotype-confirming evidence exerted more influence than stereotype-disconfirming evidence. Participants in the Stereotype-Confirming condition stereotyped more over time, but rates of stereotyping for participants in the Stereotype-Disconfirming condition remained unchanged. Participants who received no feedback, and thus no evidence, stereotyped more over time, indicating that, matching our core hypothesis, they learned from their own untested assumptions. Study 2 provided a direct replication of Study 1. In Study 3, we extended our assessment to memory. Participants made judgments and received a mix of confirmatory, disconfirmatory, and no feedback and were subsequently asked to remember the feedback they received on each trial, if any. Memory tests for the no feedback trials revealed that participants often misremembered that their untested assumptions were confirmed. Supplementing null hypothesis significance testing, Bayes Factor analyses indicated the data in Studies 1, 2, and 3 provided moderate-to-extreme evidence in favor of our hypotheses. Discussion focuses on the challenges these learning patterns create for efforts to reduce stereotyping. [ABSTRACT FROM AUTHOR]

The L0-norm constraint in sparse coding has the advantage of producing the same diversity of receptive field shapes as physiology data, but is difficult for analysis. It remains a challenging issue to understand how the diverse shapes of V1 simple cell receptive fields emerge in visual cortex. This paper presents a biologically plausible learning algorithm, named Hebbian-based mean shift, for this problem. The L0-norm constraint optimizes the number of basis functions rather than their coefficients. We report that the optimization procedure is essentially a 0-1 programming of the selection of basis functions. By assuming that the basis functions are independently selected from a basis set, we find the spatial distribution of input samples containing a special basis function has a star shape and peaks at this basis function. Thus, learning the basis functions for sparse coding with the L0-norm can be interpreted as mode detection where the basis functions are the modes of the kernel density estimate. We employ mean shift to detect modes and prove that the updating rule for the mean shift is Hebbian. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like basis functions. [ABSTRACT FROM AUTHOR]

The present paper explores the generality of the Hebb repetition effect to the learning of olfactory sequences in order to assess commonality of memory functioning across sensory modalities. Participants completed a serial-order reconstruction task comprising sequences of four olfactory stimuli. Following presentation of each sequence, participants were re-presented with the odours and were required to reconstruct their order of presentation. Surreptitious re-presentation of the repeated sequence occurred on every third trial. This order reconstruction task produced a serial-position function comprising recency only for both the non-repeated and the repeated sequences. Importantly, serial-order reconstruction for the repeated odour sequence produced improved performance for that sequence compared to the non-repeated sequences. This observation of a Hebb repetition effect for olfactory sequences further supports the proposition that sequential learning can operate amodally. [ABSTRACT FROM AUTHOR]

In this paper, we observe some important aspects of Hebbian and error-correction learning rules for complex-valued neurons. These learning rules, which were previously considered for the multi-valued neuron (MVN) whose inputs and output are located on the unit circle, are generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers. The Hebbian learning rule is also considered for the MVN with a periodic activation function. It is experimentally shown that Hebbian weights, even if they still cannot implement an input/output mapping to be learned, are better starting weights for the error-correction learning, which converges faster starting from the Hebbian weights rather than from the random ones. [ABSTRACT FROM AUTHOR]

We derive generalized spin models for the development of feedforward cortical architecture from a Hebbian synaptic learning rule in a two layer neural network with nonlinear weight constraints. Our model takes into account the effects of lateral interactions in visual cortex combining local excitation and long range effective inhibition. Our approach allows the principled derivation of developmental rules for low-dimensional feature maps, starting from high-dimensional synaptic learning rules. We incorporate the effects of smooth nonlinear constraints on net synaptic weight projected from units in the thalamic layer (the fan-out) and on the net synaptic weight received by units in the cortical layer (the fan-in). These constraints naturally couple together multiple feature maps such as orientation preference and retinotopic organization. We give a detailed illustration of the method applied to the development of the orientation preference map as a special case, in addition to deriving a model for joint pattern formation in cortical maps of orientation preference, retinotopic location, and receptive field width. We show that the combination of Hebbian learning and center-surround cortical interaction naturally leads to an orientation map development model that is closely related to the XY magnetic lattice model from statistical physics. The results presented here provide justification for phenomenological models studied in Cowan and Friedman (Advances in neural information processing systems 3, ), Thomas and Cowan (Phys Rev Lett 92(18):e188101, ) and provide a developmental model realizing the synaptic weight constraints previously assumed in Thomas and Cowan (Math Med Biol 23(2):119-138, ). [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy cognitive maps have gained considerable research interest and widely used to analyze complex systems and making decisions. Recently they have been found large applicability in diverse domains for decision support and classification tasks. A new learning paradigm for FCMs is proposed in this research work, inheriting the main aspects of ensemble based learning approaches, such as bagging and boosting. FCM ensemble learning is an approach where the model is trained using non linear Hebbian learning (NHL) algorithm and further its performance is enhanced using ensemble techniques. This work is inspired from the neural networks ensembles and it is used to learn the FCMs ensembles produced by the already known and efficient data driven NHL algorithm. The new proposed approach of FCM ensembles is applied to identification of Autism and the results are compared with those produced by data driven NHL algorithm alone for FCM training. Experimental results demonstrate that the proposed FCM ensemble algorithm works better than the NHL-based approach alone with respect to accuracy for learning FCM. [Copyright &y& Elsevier]

Abstract: Instructions are an inextricable, yet poorly understood aspect of modern human life. In this paper we propose that instruction implementation and following can be understood as fast Hebbian learning in prefrontal cortex, which trains slower pathways (e.g., cortical–basal ganglia pathways). We present a computational model of instruction following that is used to simulate key behavioral and neuroimaging data on instruction following. We discuss the relationship between our model and other models of instruction following, the predictions derived from it, and directions for future investigation. [Copyright &y& Elsevier]

Abstract: The goal of this article is to propose a new cognitive model that focuses on bottom-up learning of explicit knowledge (i.e., the transformation of implicit knowledge into explicit knowledge). This phenomenon has recently received much attention in empirical research that was not accompanied by a corresponding work effort in cognitive modeling. The new model is called TEnsor LEarning of CAusal STructure (TELECAST). In TELECAST, implicit processing is modeled using an unsupervised connectionist network (the Joint Probability EXtractor: JPEX) while explicit (causal) knowledge is implemented using a Bayesian belief network (which is built online using JPEX). Every task is simultaneously processed explicitly and implicitly and the results are integrated to provide the model output. Here, TELECAST is used to simulate a causal inference task and two serial reaction time experiments. [Copyright &y& Elsevier]

Abstract: New neurons are generated daily in the hippocampus during adult life. They are integrated into the existing neuronal circuits according to several factors such as age, physical exercise and hormonal status. At present, the role of these new neurons is debated. Computational simulations of hippocampal function allow the effects of neurogenesis to be explored, at least from a computational perspective. The present work implements a model of neurogenesis in the hippocampus with artificial neural networks, based on a standard theoretical model of biologically plausible hippocampal circuits. The performance of the model in retrieval of a variable number of patterns or memories was evaluated (episodic memory evaluation). The model increased, in a phase subsequent to initial learning, the number of granular cells by 30% relative to their initial number. In contrast to a model without neurogenesis, the retrieval of recent memories was very significantly improved, although remotes memories were only slightly affected by neurogenesis. This increase in the quality of retrieval of new memories represents a clear advantage that we attribute to the neurogenesis process. This advantage becomes more significant for higher storage loads. The model presented here suggests an important functional role of neurogenesis on learning and memory. [Copyright &y& Elsevier]

We study an adaptive controller that adjusts its internal parameters by self-organization of its interaction with the environment. We show that the parameter changes that occur in this low-level learning process can themselves provide a source of information to a higher-level context-sensitive learning mechanism. In this way, the context is interpreted in terms of the concurrent low-level learning mechanism. The dual learning architecture is studied in realistic simulations of a foraging robot and of a humanoid hand that manipulated an object. Both systems are driven by the same low-level scheme, but use the second-order information in different ways. While the low-level adaptation continues to follow a set of rigid learning rules, the second-order learning modulates the elementary behaviors and affects the distribution of the sensory inputs via the environment. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules. [ABSTRACT FROM AUTHOR]

The conflict monitoring model of M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, and J. D. Cohen (2001) triggered several research programs investigating various aspects of cognitive control. One problematic aspect of the Botvinick et al. model is that there is no clear account of how the cognitive system knows where to intervene when conflict is detected. As a result, recent findings of task-specific and context-specific (e.g., item-specific) adaptation are difficult to interpret. The difficulty with item-specific adaptation was recently pointed out by C. Blais, S. Robidoux, E. F. Risko, and D. Besner (2007), who proposed an alternative model that could account for this. However, the same problem of where the cognitive system should intervene resurfaces in a different shape in this model, and it has difficulty in accounting for the Gratton effect, a hallmark item-nonspecific effect. The authors of the current article show how these problems can be solved when cognitive control is implemented as a conflict-modulated Hebbian learning rule. [ABSTRACT FROM AUTHOR]

A new local (Hebbian) learning algorithm for artificial neurons is presented. It is shown that, in spite of its implementation simplicity, this new algorithm, applied to neurons with sigmoidal activation function, performs data clustering by finding valleys of the probability density function (PDF) of the multivariate random variables that model incoming data. Some interesting features of this new algorithm are illustrated by some experiments based on both artificial data and real world data. [ABSTRACT FROM AUTHOR]

We study a learning and recalling model of phase patterns in a two- or three-coupled BVP oscillators system with a time delay δ. The coupling strengths are modulated by the Hebbian learning rule. Assuming the first-order approximation, we calculate the optimal condition of δ for exact recall by applying the phase dynamics theory. When α = 0, where α represents the coupling from activator to inhibitor, the correlation between the learning and the retrieval phase depends on δ. When α = 1, exact recall is achieved independent of δ. The results can be explained by the phase dynamics theory. [ABSTRACT FROM AUTHOR]

Abstract: Hebbian learning is the most prominent paradigm in correlation based learning: if pre- and postsynaptic activity coincides the weight of the synapse is strengthened. Hebbian learning however, is not stable because of an autocorrelation term which causes the weights to grow exponentially. The standard solution would be to compensate the autocorrelation term. However, in this work we present a heterosynaptic learning rule which does not have an autocorrelation term and therefore does not show the instability of Hebbian learning. Consequently our heterosynaptic learning is much more stable than the classical Hebbian learning. The performance of our learning rule is demonstrated in a model which is inspired by the limbic system where an agent has to retrieve food. [Copyright &y& Elsevier]

Abstract: In primates, the area of primary visual cortex representing a fixed area of visual space decreases with increasing eccentricity. We identify visual situations to which this inhomogeneous retino-cortical mapping is well adapted and study their relevance during natural vision and development. We assume that cortical activations caused by stationary objects during self-motion along the direction of gaze travel on average with constant speed across the cortical surface, independent of retinal eccentricity. This is the case if the distribution of objects corresponds to an ellipsoid with the observer in its center. We apply the resulting flow field to train a simple network of pulse coding neurons with Hebbian learning and demonstrate that the density of learned receptive field centers is in close agreement with primate retino-cortical magnification. In addition, the model reproduces the increase of receptive field size and the decrease of its peak sensitivity with increasing eccentricity. Our results suggest that self-motion may have played an important role in the evolution of the visual system and that cortical magnification can be refined and stabilized by Hebbian learning mechanisms in ontogenesis under natural viewing conditions. [Copyright &y& Elsevier]

This paper discusses a perceptual system for natural communication between a partner robot and a human. The prediction is very important to reduce the computational cost and to extract the perceptual information for the natural communication with a human in the future. First, we propose a prediction-based perceptual control system based on spiking neurons. The proposed method is composed of four layers, namely: 1) input layer; 2) clustering layer; 3) prediction layer; and 4) perceptual module selection layer. Next, we propose an unsupervised learning method to perform the clustering of human behavior patterns. Furthermore, the robot selects perceptual modules used in the next perception according to the predicted perceptual mode. Furthermore, we show several experimental results of the communication between a partner robot and a human based on our proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Decorrelation and its higher-order generalization, independent component analysis (ICA), are fundamental and important tasks in unsupervised learning, that were studied mainly in the domain of Hebbian learning. In this paper we present a variation of the natural gradient ICA, differential ICA, where the learning relies on the concurrent change of output variables. We interpret the differential learning as the maximum likelihood estimation of parameters with latent variables represented by the random walk model. In such a framework, we derive the differential ICA algorithm and, in addition, we also present the differential decorrelation algorithm that is treated as a special instance of the differential ICA. Algorithm derivation and local stability analysis are given with some numerical experimental results. [Copyright &y& Elsevier]

Contrary to Hopfield-like networks, random recurrent neural networks (RRNN), where the couplings are random, exhibit complex dynamics (limit cycles, chaos). It is possible to store information in these networks through Hebbian learning. Eventually, learning ‘destroys’ the dynamics and leads to a fixed point attractor. We investigate here the structural changes occurring in the network through learning. We show that a simple Hebbian learning rule organizes synaptic weight redistribution on the network from an initial homogeneous and random distribution to a heterogeneous one, where strong synaptic weights preferentially assemble in triangles. Hence learning organizes the network of the large synaptic weights as a ‘small-world’ one. [ABSTRACT FROM AUTHOR]

Abstract: The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies. [Copyright &y& Elsevier]

Experiments have shown that the intrinsic excitability of neurons is not constant, but varies with physiological stimulation and during various learning paradigms. We study a model of Hebbian synaptic plasticity which is supplemented with intrinsic excitability changes. The excitability changes transcend time delays and provide a memory trace. Periods of selective enhanced excitability can thus assist in forming associations between temporally separated events, such as occur in trace conditioning. We demonstrate that simple bidirectional networks with excitability changes can learn trace conditioning paradigms. [ABSTRACT FROM AUTHOR]

E-learning settings such as on-line courses offered in China often involve large numbers of geographically dispersed students who have diverse professional background, learning preferences, and disparate learning needs. However, current e-learning and classroom teaching methods are extremely limited with respect to personalized learning as they typically provide the same content to all students. Hence, students are finding it difficult to make a decision about which learning materials best meet their personal demands, whilst instructors are finding it almost impossible to recommend personalized materials most appropriate to students since the individual study situations are disparate and hard to analyze. This paper proposes an e-learner Community Building strategy based on Hebbian Learning Rule, which helps learners to set up trusted neighbor connections to peer students with similar study situations. In addition, students can get useful recommendations for proper learning resources from their peers. Based on this theory a recommendation platform has been developed that enables a learner to enquire recommendations, vote on recommended resources and communicate with neighbor learners. Experimental results derived from real learner data have shown that this system can organize learners properly and efficiently, and the cooperative recommendation service indeed improves the students' learning achievements. [ABSTRACT FROM AUTHOR]

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong.It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possible, Hebbian and Anti-Hebbian learning. Hebbian learning should take place when the output is right, while there should be Anti-Hebbian learning when the output is wrong.For the Anti-Hebbian part of the learning rule a particular choice is made, which guarantees an adequate average neuronal activity without the need of introducing, by hand, control mechanisms like extremal dynamics. A network with realistic, i.e., non-zero threshold potentials is shown to perform its task of realizing the desired input–output relations best if it is sufficiently diluted, i.e., if only a relatively low fraction of all possible synaptic connections is realized. [Copyright &y& Elsevier]

A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of ‘path interference’, which makes that the neural net quickly forgets previously learned input–output relations is tackled by adding a Hebbian term (proportional to the learning rate ν) to the reinforcement term (proportional to δ) in the learning rule. It is shown that the number of learning steps is reduced considerably if 1/4<ν/δ<1/2, i.e. if the Hebbian term is neither too small nor too large compared to the reinforcement term. [Copyright &y& Elsevier]