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Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with singlecell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience. [ABSTRACT FROM AUTHOR]

When probed with complex stimuli that extend beyond their classical receptive field, neurons in primary visual cortex display complex and non-linear response characteristics. Sparse coding models reproduce some of the observed contextual effects, but still fail to provide a satisfactory explanation in terms of realistic neural structures and cortical mechanisms, since the connection scheme they propose consists only of interactions among neurons with overlapping input fields. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features. We derive a neurophysiologically realistic inference scheme under the constraint that neurons have direct access only to local image information. The scheme can be interpreted as a network in primary visual cortex where two neural populations are organized in different layers within orientation hypercolumns that are connected by local, short-range and long-range recurrent interactions. When trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. Subjected to contextual stimuli typically used in empirical studies, our model replicates several hallmark effects of contextual processing and predicts characteristic differences for surround modulation between the two model populations. In summary, our model provides a novel framework for contextual processing in the visual system proposing a well-defined functional role for horizontal axons and feedback projections. [ABSTRACT FROM AUTHOR]

It is well known that simple visual tasks, such as object detection or categorization, can be performed within a short period of time, suggesting the sufficiency of feed-forward visual processing. However, more complex visual tasks, such as fine-grained localization may require high-resolution information available at the early processing levels in the visual hierarchy. To access this information using a top-down approach, feedback processing would need to traverse several stages in the visual hierarchy and each step in this traversal takes processing time. In the present study, we compared the processing time required to complete object categorization and localization by varying presentation duration and complexity of natural scene stimuli. We hypothesized that performance would be asymptotic at shorter presentation durations when feed-forward processing suffices for visual tasks, whereas performance would gradually improve as images are presented longer if the tasks rely on feedback processing. In Experiment 1, where simple images were presented, both object categorization and localization performance sharply improved until 100 ms of presentation then it leveled off. These results are a replication of previously reported rapid categorization effects but they do not support the role of feedback processing in localization tasks, indicating that feed-forward processing enables coarse localization in relatively simple visual scenes. In Experiment 2, the same tasks were performed but more attention-demanding and ecologically valid images were used as stimuli. Unlike in Experiment 1, both object categorization performance and localization precision gradually improved as stimulus presentation duration became longer. This finding suggests that complex visual tasks that require visual scrutiny call for top-down feedback processing. [ABSTRACT FROM AUTHOR]

The mechanism(s) of action of most commonly used pharmacological blockers of voltage-gated ion channels are well understood; however, this knowledge is rarely considered when interpreting experimental data. Effects of blockade are often assumed to be equivalent, regardless of the mechanism of the blocker involved. Using computer simulations, we demonstrate that this assumption may not always be correct. We simulate the blockade of a persistent sodium current (INaP), proposed to underlie rhythm generation in pre-Bötzinger complex (pre-BötC) respiratory neurons, via two distinct pharmacological mechanisms: (1) pore obstruction mediated by tetrodotoxin and (2) altered inactivation dynamics mediated by riluzole. The reported effects of experimental application of tetrodotoxin and riluzole in respiratory circuits are diverse and seemingly contradictory and have led to considerable debate within the field as to the specific role of INaP in respiratory circuits. The results of our simulations match a wide array of experimental data spanning from the level of isolated pre-BötC neurons to the level of the intact respiratory network and also generate a series of experimentally testable predictions. Specifically, in this study we: (1) provide a mechanistic explanation for seemingly contradictory experimental results from in vitro studies of INaP block, (2) show that the effects of INaP block in in vitro preparations are not necessarily equivalent to those in more intact preparations, (3) demonstrate and explain why riluzole application may fail to effectively block INaP in the intact respiratory network, and (4) derive the prediction that effective block of INaP by low concentration tetrodotoxin will stop respiratory rhythm generation in the intact respiratory network. These simulations support a critical role for INaP in respiratory rhythmogenesis in vivo and illustrate the importance of considering mechanism when interpreting and simulating data relating to pharmacological blockade. [ABSTRACT FROM AUTHOR]

Motion is an essential component of everyday tactile experience: most manual interactions involve relative movement between the skin and objects. Much of the research on the neural basis of tactile motion perception has focused on how direction is encoded, but less is known about how speed is. Perceived speed has been shown to be dependent on surface texture, but previous studies used only coarse textures, which span a restricted range of tangible spatial scales and provide a limited window into tactile coding. To fill this gap, we measured the ability of human observers to report the speed of natural textures—which span the range of tactile experience and engage all the known mechanisms of texture coding—scanned across the skin. In parallel experiments, we recorded the responses of single units in the nerve and in the somatosensory cortex of primates to the same textures scanned at different speeds. We found that the perception of speed is heavily influenced by texture: some textures are systematically perceived as moving faster than are others, and some textures provide a more informative signal about speed than do others. Similarly, the responses of neurons in the nerve and in cortex are strongly dependent on texture. In the nerve, although all fibers exhibit speed-dependent responses, the responses of Pacinian corpuscle–associated (PC) fibers are most strongly modulated by speed and can best account for human judgments. In cortex, approximately half of the neurons exhibit speed-dependent responses, and this subpopulation receives strong input from PC fibers. However, speed judgments seem to reflect an integration of speed-dependent and speed-independent responses such that the latter help to partially compensate for the strong texture dependence of the former. [ABSTRACT FROM AUTHOR]

Interaural time differences (ITD) and interaural level differences (ILD) are physical cues that enable the auditory system to pinpoint the position of a sound source in space. This ability is crucial for animal communication and predator-prey interactions. The barn owl has evolved an exceptional sense of hearing and shows abilities of sound localisation that outperform most other species. So far, behavioural studies in the barn owl often used reflexive responses to investigate aspects of sound localisation. Furthermore, they predominately probed the higher frequencies of the owl’s hearing range (> 3 kHz). In the present study we used a Go/NoGo paradigm to measure the barn owl’s behavioural sound localisation acuity (expressed as the Minimum Audible Angle, MAA) as a function of stimulus type (narrow-band noise centred at 500, 1000, 2000, 4000 and 8000 Hz, and broad-band noise) and sound source position. We found significant effects of both stimulus type and sound source position on the barn owls’ MAA. The MAA improved with increasing stimulus frequency, from 14° at 500 Hz to 6° at 8000 Hz. The smallest MAA of 4° was found for broadband noise stimuli. Comparing different sound source positions revealed smaller MAAs for frontal compared to lateral stimulus presentation, irrespective of stimulus type. These results are consistent with both the known variations in physical ITDs and variation in the width of neural ITD tuning curves with azimuth and frequency. Physical and neural characteristics combine to result in better spatial acuity for frontal compared to lateral sounds and reduced localisation acuity at lower frequencies. [ABSTRACT FROM AUTHOR]

People with schizophrenia (SZ) or bipolar disorder (BD) experience dysfunction in visual processing. Dysfunctional neural tuning, in which neurons and neuronal populations are selectively activated by specific features of visual stimuli, may contribute to these deficits. Few studies have examined this possibility and there are inconsistent findings of tuning deficits in the literature. We utilized an event-related potential (ERP) paradigm to examine neural adaptation for visual objects, a measure of neural tuning whereby neurons respond less strongly to the repeated presentation of the same stimulus. Seventy-seven SZ, 53 BD, and 49 healthy comparison participants (HC) were examined. In three separate conditions, pictures of objects were presented repeatedly: the same object (SS), different objects from the same category (e.g., two different vases; SD), or different objects from different categories (e.g., a barrel and a clock, DD). Mass-univariate cluster-based permutation analyses identified electrodes and time-windows in which there were significant differences between the SS vs. DD and the SD vs. DD conditions. Mean ERP amplitudes were extracted from these clusters and analyzed for group differences. Results revealed a significant condition difference over parieto-occipital electrodes for the SS-DD comparison between 109–164 ms and for the SD-DD comparison between 78–203 ms, with larger amplitudes in the DD compared to either SS or SD condition. However, there were no significant differences in the pattern of results between groups. Thus, while we found neural adaptation effects using this ERP paradigm, we did not find evidence of group differences. Our results suggest that people with SZ or BD may not exhibit deficits in neural tuning for processing of visual objects using this EEG task with rapidly presented stimuli. However, the results are inconsistent with other studies using different methodologies (e.g., fMRI, behavioral tasks) that have found tuning deficits in people with schizophrenia. [ABSTRACT FROM AUTHOR]

Attending to visual stimuli enhances the gain of those neurons in primate visual cortex that preferentially respond to the matching locations and features (on-target gain). Although this is well suited to enhance the neuronal representation of attended stimuli, it is nonoptimal under difficult discrimination conditions, as in the presence of similar distractors. In such cases, directing attention to neighboring neuronal populations (off-target gain) has been shown to be the most efficient strategy, but although such a strategic deployment of attention has been shown behaviorally, its underlying neural mechanisms are unknown. Here, we investigated how attention affects the population responses of neurons in the middle temporal (MT) visual area of rhesus monkeys to bidirectional movement inside the neurons’ receptive field (RF). The monkeys were trained to focus their attention onto the fixation spot or to detect a direction or speed change in one of the motion directions (the “target”), ignoring the distractor motion. Population activity profiles were determined by systematically varying the patterns’ directions while maintaining a constant angle between them. As expected, the response profiles show a peak for each of the 2 motion directions. Switching spatial attention from the fixation spot into the RF enhanced the peak representing the attended stimulus and suppressed the distractor representation. Importantly, the population data show a direction-dependent attentional modulation that does not peak at the target feature but rather along the slopes of the activity profile representing the target direction. Our results show that attentional gains are strategically deployed to optimize the discriminability of target stimuli, in line with an optimal gain mechanism proposed by Navalpakkam and Itti. [ABSTRACT FROM AUTHOR]

A central issue in the study of the neural generation of respiratory rhythms is the role of the intrinsic pacemaking capabilities that some respiratory neurons exhibit. The debate on this issue has occurred in parallel to investigations of interactions among respiratory network neurons and how these contribute to respiratory behavior. In this computational study, we demonstrate how these two issues are inextricably linked. We use simulations and dynamical systems analysis to show that once a conditional respiratory pacemaker, which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, is embedded into a respiratory network, its dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node’s tuning, and that node’s outputs are dominated by network influences. Furthermore, the outputs of the respiratory central pattern generator as a whole are invariant to these changes of dynamical properties, which ensures flexible and robust performance over a wide dynamic range. [ABSTRACT FROM AUTHOR]

Our sensory environment changes constantly. Accordingly, neural systems continually adapt to the concurrent stimulus statistics to remain sensitive over a wide range of conditions. Such dynamic range adaptation (DRA) is assumed to increase both the effectiveness of the neuronal code and perceptual sensitivity. However, direct demonstrations of DRA-based efficient neuronal processing that also produces perceptual benefits are lacking. Here, we investigated the impact of DRA on spatial coding in the rodent brain and the perception of human listeners. Complex spatial stimulation with dynamically changing source locations elicited prominent DRA already on the initial spatial processing stage, the Lateral Superior Olive (LSO) of gerbils. Surprisingly, on the level of individual neurons, DRA diminished spatial tuning because of large response variability across trials. However, when considering single-trial population averages of multiple neurons, DRA enhanced the coding efficiency specifically for the concurrently most probable source locations. Intrinsic LSO population imaging of energy consumption combined with pharmacology revealed that a slow-acting LSO gain-control mechanism distributes activity across a group of neurons during DRA, thereby enhancing population coding efficiency. Strikingly, such “efficient cooperative coding” also improved neuronal source separability specifically for the locations that were most likely to occur. These location-specific enhancements in neuronal coding were paralleled by human listeners exhibiting a selective improvement in spatial resolution. We conclude that, contrary to canonical models of sensory encoding, the primary motive of early spatial processing is efficiency optimization of neural populations for enhanced source separability in the concurrent environment. [ABSTRACT FROM AUTHOR]

Orientation selectivity is a key property of primary visual cortex that contributes, downstream, to object recognition. The origin of orientation selectivity, however, has been debated for decades. It is known that on- and off-centre subcortical pathways converge onto single neurons in primary visual cortex, and that the spatial offset between these pathways gives rise to orientation selectivity. On- and off-centre pathways are intermingled, however, so it is unclear how their inputs to cortex come to be spatially segregated. We here describe a model in which the segregation occurs through Hebbian strengthening and weakening of geniculocortical synapses during the development of the visual system. Our findings include the following. 1. Neighbouring on- and off-inputs to cortex largely cancelled each other at the start of development. At each receptive field location, the Hebbian process increased the strength of one input sign at the expense of the other sign, producing a spatial segregation of on- and off-inputs. 2. The resulting orientation selectivity was precise in that the bandwidths of the orientation tuning functions fell within empirical estimates. 3. The model produced maps of preferred orientation–complete with iso-orientation domains and pinwheels–similar to those found in real cortex. 4. These maps did not originate in cortical processes, but from clustering of off-centre subcortical pathways and the relative location of neighbouring on-centre clusters. We conclude that a model with intermingled on- and off-pathways shaped by Hebbian synaptic plasticity can explain both the origin and development of orientation selectivity. [ABSTRACT FROM AUTHOR]

Neuroscience models come in a wide range of scales and specificity, from mean-field rate models to large-scale networks of spiking neurons. There are potential trade-offs between simplicity and realism, versatility and computational speed. This paper is about large-scale cortical network models, and the question we address is one of scalability: would scaling down cell density impact a network’s ability to reproduce cortical dynamics and function? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size. Reducing cell density gradually up to 50-fold, we studied changes in model behavior. Size reduction without parameter adjustment was catastrophic. Surprisingly, relatively minor compensation in synaptic weights guided by a theoretical algorithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex. Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons, and while the ability to relay feedforward inputs remained intact, reduced models showed signs of deficiency in functions that required dynamical interaction among cortical neurons. These findings are not confined to models of the visual cortex, and modelers should be aware of potential issues that accompany size reduction. Broader implications of this study include the importance of homeostatic maintenance of firing rates, and the functional consequences of feedforward versus recurrent dynamics, ideas that may shed light on other species and on systems suffering cell loss. [ABSTRACT FROM AUTHOR]

Neurons in different layers of sensory cortex generally have different functional properties. But what determines firing rates and tuning properties of neurons in different layers? Orientation selectivity in primary visual cortex (V1) is an interesting case to study these questions. Thalamic projections essentially determine the preferred orientation of neurons that receive direct input. But how is this tuning propagated though layers, and how can selective responses emerge in layers that do not have direct access to the thalamus? Here we combine numerical simulations with mathematical analyses to address this problem. We find that a large-scale network, which just accounts for experimentally measured layer and cell-type specific connection probabilities, yields firing rates and orientation selectivities matching electrophysiological recordings in rodent V1 surprisingly well. Further analysis, however, is complicated by the fact that neuronal responses emerge in a dynamic fashion and cannot be directly inferred from static neuroanatomy, as some connections tend to have unintuitive effects due to recurrent interactions and strong feedback loops. These emergent phenomena can be understood by linearizing and coarse-graining. In fact, we were able to derive a low-dimensional linear dynamical system effectively describing stimulus-driven activity layer by layer. This low-dimensional system explains layer-specific firing rates and orientation tuning by accounting for the different gain factors of the aggregate system. Our theory can also be used to design novel optogenetic stimulation experiments, thus facilitating further exploration of the interplay between connectivity and function. [ABSTRACT FROM AUTHOR]

Accurately resolving frequency components in sounds is essential for sound recognition, yet there is little direct evidence for how frequency selectivity is preserved or newly created across auditory structures. We demonstrate that prepotentials (PPs) with physiological properties resembling presynaptic potentials from broadly tuned brainstem inputs can be recorded concurrently with postsynaptic action potentials in inferior colliculus (IC). These putative brainstem inputs (PBIs) are broadly tuned and exhibit delayed and spectrally interleaved excitation and inhibition not present in the simultaneously recorded IC neurons (ICNs). A sharpening of tuning is accomplished locally at the expense of spike-timing precision through nonlinear temporal integration of broadband inputs. A neuron model replicates the finding and demonstrates that temporal integration alone can degrade timing precision while enhancing frequency tuning through interference of spectrally in- and out-of-phase inputs. These findings suggest that, in contrast to current models that require local inhibition, frequency selectivity can be sharpened through temporal integration, thus supporting an alternative computational strategy to quickly refine frequency selectivity. [ABSTRACT FROM AUTHOR]

Sensory experience elicits complex activity patterns throughout the neocortex. Projections from the neocortex converge onto the medial temporal lobe (MTL), in which distributed neocortical firing patterns are distilled into sparse representations. The precise nature of these neuronal representations is still unknown. Here, we show that population activity patterns in the MTL are governed by high levels of semantic abstraction. We recorded human single-unit activity in the MTL (4,917 units, 25 patients) while subjects viewed 100 images grouped into 10 semantic categories of 10 exemplars each. High levels of semantic abstraction were indicated by representational similarity analyses (RSAs) of patterns elicited by individual stimuli. Moreover, pattern classifiers trained to decode semantic categories generalised successfully to unseen exemplars, and classifiers trained to decode exemplar identity more often confused exemplars of the same versus different categories. Semantic abstraction and generalisation may thus be key to efficiently distill the essence of an experience into sparse representations in the human MTL. Although semantic abstraction is efficient and may facilitate generalisation of knowledge to novel situations, it comes at the cost of a loss of detail and may be central to the generation of false memories. [ABSTRACT FROM AUTHOR]

Key points: Sparse population activity is a common feature observed across cortical areas, yet the implications for sensory coding are not clear.We recorded single neuron activity in the vibrissal somatosensory cortex of awake head‐fixed mice using the cell‐attached technique.Unlike the anaesthetised condition, in awake mice a high‐velocity, piezo‐controlled whisker deflection excited only a small fraction of neurons.Manual probing of whiskers revealed that the majority of these silent neurons could be activated by specific forms of whisker–object contact.Our results suggest that sparse coding in vibrissal cortex may be due to high dimensionality of the stimulus space and narrow tuning of individual neurons. It is widely reported that superficial layers of the somatosensory cortex exhibit sparse firing. This sparseness could reflect weak feedforward sensory inputs that are not sufficient to generate action potentials in these layers. Alternatively, sparseness might reflect tuning to unknown or higher‐level complex features that are not fully explored in the stimulus space. Here, we examined these hypotheses by applying a range of vibrotactile and manual vibrissal stimuli in awake, head‐fixed mice while performing loose‐seal cell‐attached recordings from the vibrissal primary somatosensory (vS1) cortex. A high‐velocity stimulus delivered by a piezo‐electric actuator evoked activity in a small fraction of regular spiking supragranular neurons (23%) in the awake condition. However, a majority of the supragranular regular spiking neurons (84%) were driven by manual stimulation of whiskers. Our results suggest that most neurons in the superficial layers of vS1 cortex contribute to coding in the awake condition when neurons may encounter their preferred feature(s) during whisker–object interactions. Key points: Sparse population activity is a common feature observed across cortical areas, yet the implications for sensory coding are not clear.We recorded single neuron activity in the vibrissal somatosensory cortex of awake head‐fixed mice using the cell‐attached technique.Unlike the anaesthetised condition, in awake mice a high‐velocity, piezo‐controlled whisker deflection excited only a small fraction of neurons.Manual probing of whiskers revealed that the majority of these silent neurons could be activated by specific forms of whisker–object contact.Our results suggest that sparse coding in vibrissal cortex may be due to high dimensionality of the stimulus space and narrow tuning of individual neurons. [ABSTRACT FROM AUTHOR]

Persistent neural activity that encodes online mental representations plays a central role in working memory (WM). However, there has been debate regarding the number of items that can be concurrently represented in this active neural state, which is often called the “focus of attention.” Some models propose a strict single-item limit, such that just 1 item can be neurally active at once while other items are relegated to an activity-silent state. Although past studies have decoded multiple items stored in WM, these studies cannot rule out a switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8–12 Hz) activity, which encodes spatial positions held in WM. Human observers remembered 1 or 2 positions over a short delay while we recorded electroencephalography (EEG) data. Using a spatial encoding model, we reconstructed active stimulus-specific representations (channel-tuning functions [CTFs]) from the scalp distribution of alpha-band power. Consistent with past work, we found that the selectivity of spatial CTFs was lower when 2 items were stored than when 1 item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models that propose that only a single item can exist in an active state at once. Thus, our findings demonstrate that multiple items can be concurrently represented in an active neural state. [ABSTRACT FROM AUTHOR]

Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems’ emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brain’s functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity. [ABSTRACT FROM AUTHOR]

Recurrent neural networks are complex non-linear systems, capable of ongoing activity in the absence of driving inputs. The dynamical properties of these systems, in particular their long-time attractor states, are determined on the microscopic level by the connection strengths wij between the individual neurons. However, little is known to which extent network dynamics is tunable on a more coarse-grained level by the statistical features of the weight matrix. In this work, we investigate the dynamics of recurrent networks of Boltzmann neurons. In particular we study the impact of three statistical parameters: density (the fraction of non-zero connections), balance (the ratio of excitatory to inhibitory connections), and symmetry (the fraction of neuron pairs with wij = wji). By computing a ‘phase diagram’ of network dynamics, we find that balance is the essential control parameter: Its gradual increase from negative to positive values drives the system from oscillatory behavior into a chaotic regime, and eventually into stationary fixed points. Only directly at the border of the chaotic regime do the neural networks display rich but regular dynamics, thus enabling actual information processing. These results suggest that the brain, too, is fine-tuned to the ‘edge of chaos’ by assuring a proper balance between excitatory and inhibitory neural connections. [ABSTRACT FROM AUTHOR]

Miniaturized microscopes are lightweight imaging devices that allow optical recordings from neurons in freely moving animals over the course of weeks. Despite their ubiquitous use, individual neuronal responses measured with these microscopes have not been directly compared to those obtained with established in vivo imaging techniques such as bench-top two-photon microscopes. To achieve this, we performed calcium imaging in mouse primary visual cortex while presenting animals with drifting gratings. We identified the same neurons in image stacks acquired with both microscopy methods and quantified orientation tuning of individual neurons. The response amplitude and signal-to-noise ratio of calcium transients recorded upon visual stimulation were highly correlated between both microscopy methods, although influenced by neuropil contamination in miniaturized microscopy. Tuning properties, calculated for individual orientation tuned neurons, were strongly correlated between imaging techniques. Thus, neuronal tuning features measured with a miniaturized microscope are quantitatively similar to those obtained with a two-photon microscope. [ABSTRACT FROM AUTHOR]

Time is a fundamental dimension of everyday experiences. We can unmistakably sense its passage and adjust our behavior accordingly. Despite its ubiquity, the neuronal mechanisms underlying the capacity to perceive time remains unclear. Here, in two experiments using ultrahigh-field 7-Tesla (7T) functional magnetic resonance imaging (fMRI), we show that in the medial premotor cortex (supplementary motor area [SMA]) of the human brain, neural units tuned to different durations are orderly mapped in contiguous portions of the cortical surface so as to form chronomaps. The response of each portion in a chronomap is enhanced by neighboring durations and suppressed by nonpreferred durations represented in distant portions of the map. These findings suggest duration-sensitive tuning as a possible neural mechanism underlying the recognition of time and demonstrate, for the first time, that the representation of an abstract feature such as time can be instantiated by a topographical arrangement of duration-sensitive neural populations. [ABSTRACT FROM AUTHOR]

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing. [ABSTRACT FROM AUTHOR]

We use stochastic neural field theory to analyze the stimulus-dependent tuning of neural variability in ring attractor networks. We apply perturbation methods to show how the neural field equations can be reduced to a pair of stochastic nonlinear phase equations describing the stochastic wandering of spontaneously formed tuning curves or bump solutions. These equations are analyzed using a modified version of the bivariate von Mises distribution, which is well-known in the theory of circular statistics. We first consider a single ring network and derive a simple mathematical expression that accounts for the experimentally observed bimodal (or M-shaped) tuning of neural variability. We then explore the effects of inter-network coupling on stimulus-dependent variability in a pair of ring networks. These could represent populations of cells in two different layers of a cortical hypercolumn linked via vertical synaptic connections, or two different cortical hypercolumns linked by horizontal patchy connections within the same layer. We find that neural variability can be suppressed or facilitated, depending on whether the inter-network coupling is excitatory or inhibitory, and on the relative strengths and biases of the external stimuli to the two networks. These results are consistent with the general observation that increasing the mean firing rate via external stimuli or modulating drives tends to reduce neural variability. [ABSTRACT FROM AUTHOR]

Recent studies have provided evidence for a role of duration-tuned channels in the encoding of duration. Duration encoding in these channels is thought to reflect the time between responses to the onset and offset of an event. This notion is in apparent conflict with studies that demonstrate that the perceived duration of an event can vary independently from the time separating its perceived onset and offset. Instead, these studies suggest that duration encoding is sensitive to other temporal aspects of a sensory event. In the current study, we investigated whether duration-tuned channels encode duration based on the time between the on- and offset of an event (onset-offset duration), or if they encode a duration corresponding to the perceived duration of that event. We used a duration illusion to dissociate onset-offset duration and perceived duration and measured whether repeated exposure to illusion-inducing stimuli caused adaptation to the onset-offset duration or the perceived duration of these illusion-inducing stimuli. We report clear evidence for adaptation to the onset-offset duration of illusion-inducing stimuli. This finding supports the notion that duration-tuned mechanisms respond to the time between the onset and offset of an event, without necessarily reflecting the duration perceived, and eventually reported by the participant. Implications for the duration channel model and the mechanisms underlying duration illusions are discussed. [ABSTRACT FROM AUTHOR]

Our current understanding of how the brain segregates auditory scenes into meaningful objects is in line with a Gestaltism framework. These Gestalt principles suggest a theory of how different attributes of the soundscape are extracted then bound together into separate groups that reflect different objects or streams present in the scene. These cues are thought to reflect the underlying statistical structure of natural sounds in a similar way that statistics of natural images are closely linked to the principles that guide figure-ground segregation and object segmentation in vision. In the present study, we leverage inference in stochastic neural networks to learn emergent grouping cues directly from natural soundscapes including speech, music and sounds in nature. The model learns a hierarchy of local and global spectro-temporal attributes reminiscent of simultaneous and sequential Gestalt cues that underlie the organization of auditory scenes. These mappings operate at multiple time scales to analyze an incoming complex scene and are then fused using a Hebbian network that binds together coherent features into perceptually-segregated auditory objects. The proposed architecture successfully emulates a wide range of well established auditory scene segregation phenomena and quantifies the complimentary role of segregation and binding cues in driving auditory scene segregation. [ABSTRACT FROM AUTHOR]

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects. [ABSTRACT FROM AUTHOR]

Frequency analysis is a fundamental function of the auditory system, and it is essential to study the auditory response properties using behavior-related sounds. Our previous study has shown that the inferior collicular (IC) neurons of CF-FM (constant frequency-frequency modulation) bats could be classified into single-on (SO) and double-on (DO) neurons under CF-FM stimulation. Here, we employed Pratt's roundleaf bats, Hipposideros pratti, to investigate the frequency selectivity of SO and DO neurons in response to CF and behavior-related CF-FM sounds using in vivo extracellular recordings. The results demonstrated that the bandwidths (BWs) of iso-frequency tuning curves had no significant differences between the SO and the DO neurons when stimulated by CF sounds. However, the SO neurons had significant narrower BWs than DO neurons when stimulated with CF-FM sounds. In vivo intracellular recordings showed that both SO and DO neurons had significantly shorter post-spike hyperpolarization latency and excitatory duration in response to CF-FM in comparison to CF stimuli, suggesting that the FM component had an inhibitory effect on the responses to the CF component. These results suggested that SO neurons had higher frequency selectivity than DO neurons under behavior-related CF-FM stimulation, making them suitable for detecting frequency changes during echolocation. [ABSTRACT FROM AUTHOR]

We study a self-organising neural network model of how visual representations in the primate dorsal visual pathway are transformed from an eye-centred to head-centred frame of reference. The model has previously been shown to robustly develop head-centred output neurons with a standard trace learning rule, but only under limited conditions. Specifically it fails when incorporating visual input neurons with monotonic gain modulation by eye-position. Since eye-centred neurons with monotonic gain modulation are so common in the dorsal visual pathway, it is an important challenge to show how efferent synaptic connections from these neurons may self-organise to produce head-centred responses in a subpopulation of postsynaptic neurons. We show for the first time how a variety of modified, yet still biologically plausible, versions of the standard trace learning rule enable the model to perform a coordinate transformation from eye-centred to head-centred reference frames when the visual input neurons have monotonic gain modulation by eye-position. [ABSTRACT FROM AUTHOR]

Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input. [ABSTRACT FROM AUTHOR]

The precision with which visual information can be recalled from working memory declines as the number of items in memory increases. This finding has been explained in terms of the distribution of a limited representational resource between items. Here we investigated how the sensory strength of memoranda affects resource allocation. We manipulated signal strength of an orientation stimulus in two ways: we varied the internal (sensory) noise by adjusting stimulus contrast, and varied the external (stimulus) noise by altering the within-stimulus variability. Both manipulations had similar effects on the precision with which the orientation could be recalled, but differed in their impact on memory for other stimuli. These results indicate that increasing internal noise released resources that could be used to store other stimuli more precisely; increasing external noise had no such effect. We show that these observations can be captured by a simple neural model of working memory encoding, in which spiking activity takes on the role of the limited resource. [ABSTRACT FROM AUTHOR]

Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state. [ABSTRACT FROM AUTHOR]

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations. [ABSTRACT FROM AUTHOR]

Despite a large body of research on response properties of neurons in the inferior temporal (IT) cortex, studies to date have not yet produced quantitative feature descriptions that can predict responses to arbitrary objects. This deficit in the research prevents a thorough understanding of object representation in the IT cortex. Here we propose a fragment-based approach for finding quantitative feature descriptions of face neurons in the IT cortex. The development of the proposed method was driven by the assumption that it is possible to recover features from a set of natural image fragments if the set is sufficiently large. To find the feature from the set, we compared object responses predicted from each fragment and responses of neurons to these objects, and search for the fragment that revealed the highest correlation with neural object responses. Prediction of object responses of each fragment was made by normalizing Euclidian distance between the fragment and each object to 0 to 1 such that the smaller distance gives the higher value. The distance was calculated at the space where images were transformed to a local orientation space by a Gabor filter and a local max operation. The method allowed us to find features with a correlation coefficient between predicted and neural responses of 0.68 on average (number of object stimuli, 104) from among 560,000 feature candidates, reliably explaining differential responses among faces as well as a general preference for faces over to non-face objects. Furthermore, predicted responses of the resulting features to novel object images were significantly correlated with neural responses to these images. Identification of features comprising specific, moderately complex combinations of local orientations and colors enabled us to predict responses to upright and inverted faces, which provided a possible mechanism of face inversion effects. (292/300). [ABSTRACT FROM AUTHOR]

The cerebellum is involved in coordinating motor behaviour, but how the cerebellar network regulates locomotion is still not well understood. We characterised the activity of putative cerebellar Purkinje cells, Golgi cells and mossy fibres in awake mice engaged in an active locomotion task, using high-density silicon electrode arrays. Analysis of the activity of over 300 neurons in response to locomotion revealed that the majority of cells (53%) were significantly modulated by phase of the stepping cycle. However, in contrast to studies involving passive locomotion on a treadmill, we found that a high proportion of cells (45%) were tuned to the speed of locomotion, and 19% were tuned to yaw movements. The activity of neurons in the cerebellar vermis provided more information about future speed of locomotion than about past or present speed, suggesting a motor, rather than purely sensory, role. We were able to accurately decode the speed of locomotion with a simple linear algorithm, with only a relatively small number of well-chosen cells needed, irrespective of cell class. Our observations suggest that behavioural state modulates cerebellar sensorimotor integration, and advocate a role for the cerebellar vermis in control of high-level locomotor kinematic parameters such as speed and yaw. [ABSTRACT FROM AUTHOR]

The last decade has seen the emergence of new views about the mechanisms underlying specificity (or, conversely, generalization) of visual skill learning. Here, we trained participants at orientation discrimination paradigm at a peripheral position to induce position and orientation specificity and to test its underlying mechanisms. Specifically, we aimed to test whether the within-quadrant spatial gradient of generalization is determined by cortical magnification, which would show that retinotopic plasticity contributes to learning and specificity. Additionally, we aimed to test whether late parts of the learning relate differently to specificity compared to early parts. This is relevant in the context of double training papers, which suggest that rule-based mechanisms of specificity in fast, early learning also would apply to late, slower learning. Our data showed partial but significant position and orientation specificity within quadrants. Interestingly, specificity was greatest for those participants who had continued to show threshold decreases during the last five sessions of training (late, asymptotic learning). Performance gains during early learning were less related to specificity. A trend for skill to spread over larger distances towards periphery than towards central vision suggested contributions to transfer of early visual areas showing cortical magnification of central vision. Control experiments however did not support this hypothesis. In summary, our study demonstrates significant specificity after extensive perceptual learning, and indicates that asymptotic learning recruits specific mechanisms that promote specificity, and that may not be recruited yet in early parts of the learning. The contributions of different mechanisms to early and late learning suggests that following these different learning periods, generalization relies on different principles and is subjected to different limits. [ABSTRACT FROM AUTHOR]

A key component of working memory is the ability to remember multiple items simultaneously. To understand how the human brain maintains multiple items in memory, we examined direct brain recordings of neural oscillations from neurosurgical patients as they performed a working memory task. We analyzed the data to identify the neural representations of individual memory items by identifying recording sites with broadband gamma activity that varied according to the identity of the letter a subject viewed. Next, we tested a previously proposed model of working memory, which had hypothesized that the neural representations of individual memory items sequentially occurred at different phases of the theta/alpha cycle. Consistent with this model, the phase of the theta/alpha oscillation when stimulus-related gamma activity occurred during maintenance reflected the order of list presentation. These results suggest that working memory is organized by a cortical phase code coordinated by coupled theta/alpha and gamma oscillations and, more broadly, provide support for the serial representation of items in working memory. [ABSTRACT FROM AUTHOR]

Whereas optogenetic techniques have proven successful in their ability to manipulate neuronal populations—with high spatial and temporal fidelity—in species ranging from insects to rodents, significant obstacles remain in their application to nonhuman primates (NHPs). Robust optogenetics-activated behavior and long-term monitoring of target neurons have been challenging in NHPs. Here, we present a method for all-optical interrogation (AOI), integrating optical stimulation and simultaneous two-photon (2P) imaging of neuronal populations in the primary visual cortex (V1) of awake rhesus macaques. A red-shifted channel-rhodopsin transgene (ChR1/VChR1 [C1V1]) and genetically encoded calcium indicators (genetically encoded calmodulin protein [GCaMP]5 or GCaMP6s) were delivered by adeno-associated viruses (AAVs) and subsequently expressed in V1 neuronal populations for months. We achieved optogenetic stimulation using both single-photon (1P) activation of neuronal populations and 2P activation of single cells, while simultaneously recording 2P calcium imaging in awake NHPs. Optogenetic manipulations of V1 neuronal populations produced reliable artificial visual percepts. Together, our advances show the feasibility of precise and stable AOI of cortical neurons in awake NHPs, which may lead to broad applications in high-level cognition and preclinical testing studies. [ABSTRACT FROM AUTHOR]

Detecting regular patterns in the environment, a process known as statistical learning, is essential for survival. Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short (seconds to minutes) temporal windows. Here, we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously, in a temporally sparse manner, across windows of minutes to hours. Using a combination of behavioral, electrophysiological, and molecular approaches, we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding. Analysis of population activity (structural tuning) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies. The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination. Gain modulation was accompanied by changes in gene and protein expression, indicative of long-term plasticity. Physiological changes were largely independent of corticofugal feedback, and no changes were seen in upstream cochlear nucleus responses, suggesting a key role of the auditory midbrain in sensory gating. Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning. Using longer timescales than previously explored, the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns, a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through. [ABSTRACT FROM AUTHOR]

We theoretically investigate the signal-to-noise ratio (SNR) of a parallel array of leaky integrate-and-fire (LIF) neurons that receives a weak periodic signal and uses spatially nearest neighbor correlated noise. By using linear response theory, we derive the analytic expression of the SNR. The results show that the amplitude of internal noise can be increased up to an optimal value, which corresponds to a maximum SNR. Given the existence of spatially nearest neighbor correlated noise in the neural ensemble, the SNR gain of the collective ensemble response can exceed unity, especially for a negative correlation. This nonlinear collective phenomenon of SNR gain amplification may be related to the array stochastic resonance. In addition, we show that the SNR can be improved by varying the number of neurons, frequency, and amplitude of the weak periodic signal. We expect that this investigation will be useful for both controlling the collective response of neurons and enhancing weak signal transmission. [ABSTRACT FROM AUTHOR]

Directional mechanoreception by hair cells is transmitted to the brain via afferent neurons to enable postural control and rheotaxis. Neuronal tuning to individual directions of mechanical flow occurs when each peripheral axon selectively synapses with multiple hair cells of identical planar polarization. How such mechanosensory labeled lines are established and maintained remains unsolved. Here, we use the zebrafish lateral line to reveal that asymmetric activity of the transcription factor Emx2 diversifies hair cell identity to instruct polarity-selective synaptogenesis. Unexpectedly, presynaptic scaffolds and coherent hair cell orientation are dispensable for synaptic selectivity, indicating that epithelial planar polarity and synaptic partner matching are separable. Moreover, regenerating axons recapitulate synapses with hair cells according to Emx2 expression but not global orientation. Our results identify a simple cellular algorithm that solves the selectivity task even in the presence of noise generated by the frequent receptor cell turnover. They also suggest that coupling connectivity patterns to cellular identity rather than polarity relaxes developmental and evolutionary constraints to innervation of organs with differing orientation. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are non-coding RNAs originally involved in RNA silencing and post-transcriptional regulation of gene expression. We have shown in previous work that the miRNA let-7b can act as a signalling molecule for Toll-like receptor 7, thereby initiating innate immune pathways and apoptosis in the central nervous system. Here, we investigated whether different members of the miRNA family let-7, abundantly expressed in the brain, are released into the human cerebrospinal fluid (CSF) and whether quantitative differences in let-7 copies exist in neurodegenerative diseases. RNA isolated from CSF of patients with Alzheimer´s disease (AD) and from control patients with frontotemporal lobe dementia (FTLD), major depressive episode (MDE) without clinical or neurobiological signs of AD, and healthy individuals, was reverse transcribed with primers against nine let-7 family members, and miRNAs were quantified and analyzed comparatively by quantitative PCR. let-7 miRNAs were present in CSF from patients with AD, FTLD, MDE, and healthy controls. However, the amount of individual let-7 miRNAs in the CSF varied substantially. CSF from AD patients contained higher amounts of let-7b and let-7e compared to healthy controls, while no differences were observed regarding the other let-7 miRNAs. No increase in let-7b and let-7e was detected in CSF from FTLD patients, while in CSF from MDE patients, let-7b and let-7e copy levels were elevated. In CSF from AD patients, let-7b and let-7e were associated with extracellular vesicles. let-7 family members present in the CSF mediated neurotoxicity in vitro, albeit to a variable extent. Taken together, neurotoxic let-7 miRNAs are differentially and specifically released in AD, but also in MDE patients. Thus, these miRNAs may mirror common neuropathological paths and by this serve to unscramble mechanisms of different neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, suggesting larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. Variance explained in total population activity scales with the number of neurons imaged, suggesting larger sample sizes are required to fully capture local network interactions. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif. [ABSTRACT FROM AUTHOR]

Mormyrid fish rely on reafferent input for active electrolocation. Their electrosensory input consists of phase and amplitude information. These are encoded by differently tuned receptor cells within the Mormyromasts, A- and B-cells, respectively, which are distributed over the animal’s body. These convey their information to two topographically ordered medullary zones in the electrosensory lateral line lobe (ELL). The so-called medial zone receives only amplitude information, while the dorsolateral zone receives amplitude and phase information. Using both sources of information, Mormyrid fish can disambiguate electrical impedances. Where and how this disambiguation takes place is presently unclear. We here investigate phase-sensitivity downstream from the electroreceptors. We provide first evidence of phase-sensitivity in the medial zone of ELL. In this zone I-cells consistently decreased their rate to positive phase-shifts (6 of 20 cells) and increased their rate to negative shifts (11/20), while E-cells of the medial zone (3/9) responded oppositely to I-cells. In the dorsolateral zone the responses of E- and I-cells were opposite to those found in the medial zone. Tracer injections revealed interzonal projections that interconnect the dorsolateral and medial zones in a somatotopic manner. In summary, we show that phase information is processed differently in the dorsolateral and the medial zones. This is the first evidence for a mechanism that enhances the contrast between two parallel sensory channels in Mormyrid fish. This could be beneficial for impedance discrimination that ultimately must rely on a subtractive merging of these two sensory streams. [ABSTRACT FROM AUTHOR]

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city—São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce. [ABSTRACT FROM AUTHOR]

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains. [ABSTRACT FROM AUTHOR]

Traditionally, the left hemisphere has been thought to extract mainly verbal patterns of information, but recent evidence has shown that the left Inferior Frontal Gyrus (IFG) is active during inductive reasoning in both the verbal and spatial domains. We aimed to understand whether the left IFG supports inductive reasoning in a domain-specific or domain-general fashion. To do this we used Multi-Voxel Pattern Analysis to decode the representation of domain during a rule search task. Thirteen participants were asked to extract the rule underlying streams of letters presented in different spatial locations. Each rule was either verbal (letters forming words) or spatial (positions forming geometric figures). Our results show that domain was decodable in the left prefrontal cortex, suggesting that this region represents domain-specific information, rather than processes common to the two domains. A replication study with the same participants tested two years later confirmed these findings, though the individual representations changed, providing evidence for the flexible nature of representations. This study extends our knowledge on the neural basis of goal-directed behaviors and on how information relevant for rule extraction is flexibly mapped in the prefrontal cortex. [ABSTRACT FROM AUTHOR]

Understanding the relationship between external stimuli and the spiking activity of cortical populations is a central problem in neuroscience. Dense recurrent connectivity in local cortical circuits can lead to counterintuitive response properties, raising the question of whether there are simple arithmetical rules for relating circuits’ connectivity structure to their response properties. One such arithmetic is provided by the mean field theory of balanced networks, which is derived in a limit where excitatory and inhibitory synaptic currents precisely balance on average. However, balanced network theory is not applicable to some biologically relevant connectivity structures. We show that cortical circuits with such structure are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations, but maintained at a global level. This amplification, which can be quantified by a linear correction to the classical mean field theory of balanced networks, explains several response properties observed in cortical recordings and provides fundamental insights into the relationship between connectivity structure and neural responses in cortical circuits. [ABSTRACT FROM AUTHOR]