Functional heterogeneity in neighboring neurons of cat primary visual cortex in response to both artificial and natural stimuli

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations

Neurons in the CA1 area of the mouse hippocampus encode the position of the animal in an environment. However, given the variability in individual neurons responses, the accuracy of this code is still poorly understood. It was proposed that downstream areas could achieve high spatial accuracy by integrating the activity of thousands of neurons, but theoretical studies point to shared fluctuations in the firing rate as a potential limitation. Using high-throughput calcium imaging in freely moving mice, we demonstrated the limiting factors in the accuracy of the CA1 spatial code. We found that noise correlations in the hippocampus bound the estimation error of spatial coding to ~10 cm (the size of a mouse). Maximal accuracy was obtained using approximately [300-1400] neurons, depending on the animal. These findings reveal intrinsic limits in the brain's representations of space and suggest that single neurons downstream of the hippocampus can extract maximal spatial information from several hundred inputs.

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.

Accounting for Biases in the Estimation of Neuronal Signal Correlation

Signal correlation (rs) is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, [Formula: see text], between the average responses of two neurons to a set of stimuli suffers from confounding biases. The estimate [Formula: see text] can be downwardly biased by trial-to-trial variability and also upwardly biased by trial-to-trial correlation between neurons, and these biases can hide important aspects of neural coding. Here we provide analytic results on the source of these biases and explore them for ranges of parameters that are relevant for electrophysiological experiments. We then provide corrections for these biases that we validate in simulation. Furthermore, we apply these corrected estimators to make the following novel experimental observation in cortical area MT: pairs of nearby neurons that are strongly tuned for motion direction tend to have high signal correlation, and pairs that are weakly tuned tend to have low signal correlation. We dismiss a trivial explanation for this and find that an analogous trend holds for orientation tuning in the primary visual cortex. We also consider the potential consequences for encoding whereby the association of signal correlation and tuning strength naturally regularizes the dimensionality of downstream computations.SIGNIFICANCE STATEMENT Fundamental to how cortical neurons encode information about the environment is their functional similarity, that is, the redundancy in what they encode and their shared noise. These properties have been extensively studied theoretically and experimentally throughout the nervous system, but here we show that a common estimator of functional similarity has confounding biases. We characterize these biases and provide estimators that do not suffer from them. Using our improved estimators, we demonstrate a novel result, that is, there is a positive relationship between tuning curve similarity and amplitude for nearby neurons in the visual cortical motion area MT. We provide a simple stochastic model explaining this relationship and discuss how it would naturally regularize the dimensionality of neural encoding.

Chromatic micromaps in primary visual cortex

The clustering of neurons with similar response properties is a conspicuous feature of neocortex. In primary visual cortex (V1), maps of several properties like orientation preference are well described, but the functional architecture of color, central to visual perception in trichromatic primates, is not. Here we used two-photon calcium imaging in macaques to examine the fine structure of chromatic representation and found that neurons responsive to spatially uniform, chromatic stimuli form unambiguous clusters that coincide with blobs. Further, these responsive groups have marked substructure, segregating into smaller ensembles or micromaps with distinct chromatic signatures that appear columnar in upper layer 2/3. Spatially structured chromatic stimuli revealed maps built on the same micromap framework but with larger subdomains that go well beyond blobs. We conclude that V1 has an architecture for color representation that switches between blobs and a combined blob/interblob system based on the spatial content of the visual scene.

Stimulus feature maps are found in primary visual cortex of many species. Here the authors show color maps in trichromatic primates containing segregated ensembles of neurons with distinct chromatic signatures that associate with cortical modules known as blobs.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex

The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits ("macrocolumns"), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.

Cell-Type-Specific Thalamocortical Inputs Constrain Direction Map Formation in Visual Cortex

Finding the relationship between individual cognitive functions and cell-type-specific neuronal circuits is a central topic in neuroscience. In cats, the lateral geniculate nucleus (LGN) contains several cell types carrying spatially and temporally precise visual information. Whereas LGN cell types lack selectivity for motion direction, neurons in the primary visual cortex (area 17) exhibit sharp direction selectivity. Whether and how such de novo formation of direction selectivity depends on LGN cell types remains unknown. Here, we addressed this question using in vivo two-photon calcium imaging in cat area 17, which consists of two compartments receiving different combinations of inputs from the LGN cell types. The direction map in area 17 showed unique fragmented organization and was present only in small and distributed cortical domains. Moreover, direction-selective domains preferentially localized in specific compartments receiving Y and W inputs carrying low spatial frequency visual information, indicating that cell-type-specific thalamocortical projections constrain the formation of direction selectivity.

Spiking Noise and Information Density of Neurons in Visual Area V2 of Infant Monkeys

Encoding of visual information requires precisely timed spiking activity in the network of cortical neurons; irregular spiking can interfere with information processing especially for low-contrast images. The vision of newborn infants is impoverished. An infant's contrast sensitivity is low and the ability to discriminate complex stimuli is poor. The neural mechanisms that limit the visual capacities of infants are a matter of debate. Here we asked whether noisy spiking and/or crude information processing in visual cortex limit infant vision. Since neurons beyond the primary visual cortex (V1) have rarely been studied in neonates or infants, we focused on the firing pattern of neurons in visual area V2, the earliest extrastriate visual area of both male and female macaque monkeys (Maccaca mulatta). For eight stimulus contrasts ranging from 0% to 80%, we analyzed spiking irregularity by calculating the square of the coefficient of variation (CV²) in interspike intervals, the trial-to-trial fluctuation in spiking (Fano factor), and the amount of information on contrast conveyed by each spiking (information density). While the contrast sensitivity of infant neurons was reduced as expected, spiking noise, both the magnitude of spiking irregularity and the trial-to-trial fluctuations, was much lower in the spike trains of infant V2 neurons compared with those of adults. However, information density for V2 neurons was significantly lower in infants. Our results suggest that poor contrast sensitivity combined with lower information density of extrastriate neurons, despite their lower spiking noise, may limit behaviorally determined contrast sensitivity soon after birth.SIGNIFICANCE STATEMENT Despite >50 years of investigations on the postnatal development of the primary visual cortex (V1), cortical mechanisms that may limit infant vision are still unclear. We investigated the quality and strength of neuronal firing in primate visual area V2 by analyzing contrast sensitivity, spiking variability, and the amount of information on contrast conveyed by each action potential (information density). Here we demonstrate that the firing rate, contrast sensitivity, and dynamic range of V2 neurons were depressed in infants compared with adults. Although spiking noise was less, information density was lower in infant V2. Impoverished neuronal drive and lower information density in extrastriate visual areas, despite lower spiking noise, largely explain the impoverished visual sensitivity of primates near birth.

Specific excitatory connectivity for feature integration in mouse primary visual cortex

Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a 'like-to-like' scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a 'feature binding' scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.

Universal transition from unstructured to structured neural maps

Neurons sharing similar features are often selectively connected with a higher probability and should be located in close vicinity to save wiring. Selective connectivity has, therefore, been proposed to be the cause for spatial organization in cortical maps. Interestingly, orientation preference (OP) maps in the visual cortex are found in carnivores, ungulates, and primates but are not found in rodents, indicating fundamental differences in selective connectivity that seem unexpected for closely related species. Here, we investigate this finding by using multidimensional scaling to predict the locations of neurons based on minimizing wiring costs for any given connectivity. Our model shows a transition from an unstructured salt-and-pepper organization to a pinwheel arrangement when increasing the number of neurons, even without changing the selectivity of the connections. Increasing neuronal numbers also leads to the emergence of layers, retinotopy, or ocular dominance columns for the selective connectivity corresponding to each arrangement. We further show that neuron numbers impact overall interconnectivity as the primary reason for the appearance of neural maps, which we link to a known phase transition in an Ising-like model from statistical mechanics. Finally, we curated biological data from the literature to show that neural maps appear as the number of neurons in visual cortex increases over a wide range of mammalian species. Our results provide a simple explanation for the existence of salt-and-pepper arrangements in rodents and pinwheel arrangements in the visual cortex of primates, carnivores, and ungulates without assuming differences in the general visual cortex architecture and connectivity.

Bidirectional Shifting Effects of the Sound Intensity on the Best Frequency in the Rat Auditory Cortex

Frequency and intensity are two independent attributes of sound stimuli. Psychoacoustic studies have found that the sound intensity can affect the perception of frequency; however, the underlying neuronal mechanism remains largely unknown. To investigate if and how the sound level affects the frequency coding for auditory cortical neurons, we recorded the activities of neuronal ensembles and single neurons, as well as the synaptic input evoked by pure tones of different frequency and intensity combinations, in layer 4 of the rat primary auditory cortex. We found that the best frequency (BF) shifted bidirectionally with the increases in intensity. Specifically, the BF of neurons with a low characteristic frequency (CF) shifted lower, whereas the BF of neurons with a higher CF shifted higher. Meanwhile, we found that these shifts in the BF can lead to the expansion of high- and low-frequency areas in the tonotopic map, increasing the evenness of the BF distribution at high intensities. Our results revealed that the frequency tuning can bidirectionally shift with an increase in the sound intensity at both the cellular and population level. This finding is consistent with the perceptual illusions observed in humans and could provide a potential mechanism for this psychoacoustic effect.

Synaptic connections formed by patchy projections of pyramidal cells in the superficial layers of cat visual cortex

The present study is the first to describe quantitatively the patterns of synaptic connections made by the patchy network of pyramidal cell axons in the superficial layers of cat V1 in relation to the orientation map. Intrinsic signal imaging of the orientation map was combined with 3D morphological reconstructions of physiologically-characterized neurons at light and electron microscope levels. A Similarity Index (SI) expressed the similarity of the orientation domain of a given bouton cluster to that of its parent dendritic tree. Six pyramidal cells whose axons had a wide range of SIs were examined. Boutons were sampled from five local and five distal clusters, and from the linear segments that link the clusters. The synaptic targets were reconstructed by serial section electron microscopy. Of the 233 synapses examined, 182 synapses were formed with spiny neurons, the remainder with smooth neurons. The proportion of smooth neurons that were synaptic targets varied greatly (from 0 to 50%) between the cluster samples, but was not correlated with the SI. The postsynaptic density sizes were similar for synapses in local and distal clusters, regardless of their SI. This heterogeneity in the synaptic targets of single cells within the superficial layers is a network feature well-suited for context-dependent processing.

Information Accumulation over Time in Monkey Inferior Temporal Cortex Neurons Explains Pattern Recognition Reaction Time under Visual Noise

We recognize objects even when they are partially degraded by visual noise. We studied the relation between the amount of visual noise (5, 10, 15, 20, or 25%) degrading 8 black-and-white stimuli and stimulus identification in 2 monkeys performing a sequential delayed match-to-sample task. We measured the accuracy and speed with which matching stimuli were identified. The performance decreased slightly (errors increased) as the amount of visual noise increased for both monkeys. The performance remained above 80% correct, even with 25% noise. However, the reaction times markedly increased as the noise increased, indicating that the monkeys took progressively longer to decide what the correct response would be as the amount of visual noise increased, showing that the monkeys trade time to maintain accuracy. Thus, as time unfolds the monkeys act as if they are accumulating the information and/or testing hypotheses about whether the test stimulus is likely to be a match for the sample being held in short-term memory. We recorded responses from 13 single neurons in area TE of the 2 monkeys. We found that stimulus-selective information in the neuronal responses began accumulating when the match stimulus appeared. We found that the greater the amount of noise obscuring the test stimulus, the more slowly stimulus-related information by the 13 neurons accumulated. The noise induced slowing was about the same for both behavior and information. These data are consistent with the hypothesis that area TE neuron population carries information about stimulus identity that accumulates over time in such a manner that it progressively overcomes the signal degradation imposed by adding visual noise.

Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex

Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus.

A central goal in systems neuroscience is to understand how large populations of neurons work together to enable us to sense, to reason, and to act. To go beyond single-neuron and pairwise analyses, recent studies have applied dimensionality reduction methods to neural population activity to reveal tantalizing evidence of neural mechanisms underlying a wide range of brain functions. To aid in interpreting the outputs of dimensionality reduction, it is important to vary the inputs to a brain area and ask whether the outputs of dimensionality reduction change in a sensible manner, which has not yet been shown. In this study, we recorded the activity of tens of neurons in the primary visual cortex (V1) of macaque monkeys while presenting different visual stimuli. We found that the dimensionality of the population activity grows with stimulus complexity, and that the population responses to different stimuli occupy similar dimensions of the population firing rate space, in accordance with the visual stimuli themselves. For comparison, we applied the same analysis methods to the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Overall, we found dimensionality reduction to yield interpretable results, providing encouragement for the use of dimensionality reduction in other brain areas.

Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.

Correlations and Neuronal Population Information

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex

Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.

Early monocular defocus disrupts the normal development of receptive-field structure in V2 neurons of macaque monkeys

Experiencing different quality images in the two eyes soon after birth can cause amblyopia, a developmental vision disorder. Amblyopic humans show the reduced capacity for judging the relative position of a visual target in reference to nearby stimulus elements (position uncertainty) and often experience visual image distortion. Although abnormal pooling of local stimulus information by neurons beyond striate cortex (V1) is often suggested as a neural basis of these deficits, extrastriate neurons in the amblyopic brain have rarely been studied using microelectrode recording methods. The receptive field (RF) of neurons in visual area V2 in normal monkeys is made up of multiple subfields that are thought to reflect V1 inputs and are capable of encoding the spatial relationship between local stimulus features. We created primate models of anisometropic amblyopia and analyzed the RF subfield maps for multiple nearby V2 neurons of anesthetized monkeys by using dynamic two-dimensional noise stimuli and reverse correlation methods. Unlike in normal monkeys, the subfield maps of V2 neurons in amblyopic monkeys were severely disorganized: subfield maps showed higher heterogeneity within each neuron as well as across nearby neurons. Amblyopic V2 neurons exhibited robust binocular suppression and the strength of the suppression was positively correlated with the degree of hereogeneity and the severity of amblyopia in individual monkeys. Our results suggest that the disorganized subfield maps and robust binocular suppression of amblyopic V2 neurons are likely to adversely affect the higher stages of cortical processing resulting in position uncertainty and image distortion.

Triplet correlations among similarly tuned cells impact population coding

Which statistical features of spiking activity matter for how stimuli are encoded in neural populations? A vast body of work has explored how firing rates in individual cells and correlations in the spikes of cell pairs impact coding. Recent experiments have shown evidence for the existence of higher-order spiking correlations, which describe simultaneous firing in triplets and larger ensembles of cells; however, little is known about their impact on encoded stimulus information. Here, we take a first step toward closing this gap. We vary triplet correlations in small (approximately 10 cell) neural populations while keeping single cell and pairwise statistics fixed at typically reported values. This connection with empirically observed lower-order statistics is important, as it places strong constraints on the level of triplet correlations that can occur. For each value of triplet correlations, we estimate the performance of the neural population on a two-stimulus discrimination task. We find that the allowed changes in the level of triplet correlations can significantly enhance coding, in particular if triplet correlations differ for the two stimuli. In this scenario, triplet correlations must be included in order to accurately quantify the functionality of neural populations. When both stimuli elicit similar triplet correlations, however, pairwise models provide relatively accurate descriptions of coding accuracy. We explain our findings geometrically via the skew that triplet correlations induce in population-wide distributions of neural responses. Finally, we calculate how many samples are necessary to accurately measure spiking correlations of this type, providing an estimate of the necessary recording times in future experiments.

Diverse coupling of neurons to populations in sensory cortex

A large population of neurons can, in principle, produce an astronomical number of distinct firing patterns. In cortex, however, these patterns lie in a space of lower dimension, as if individual neurons were "obedient members of a huge orchestra". Here we use recordings from the visual cortex of mouse (Mus musculus) and monkey (Macaca mulatta) to investigate the relationship between individual neurons and the population, and to establish the underlying circuit mechanisms. We show that neighbouring neurons can differ in their coupling to the overall firing of the population, ranging from strongly coupled 'choristers' to weakly coupled 'soloists'. Population coupling is largely independent of sensory preferences, and it is a fixed cellular attribute, invariant to stimulus conditions. Neurons with high population coupling are more strongly affected by non-sensory behavioural variables such as motor intention. Population coupling reflects a causal relationship, predicting the response of a neuron to optogenetically driven increases in local activity. Moreover, population coupling indicates synaptic connectivity; the population coupling of a neuron, measured in vivo, predicted subsequent in vitro estimates of the number of synapses received from its neighbours. Finally, population coupling provides a compact summary of population activity; knowledge of the population couplings of n neurons predicts a substantial portion of their n(2) pairwise correlations. Population coupling therefore represents a novel, simple measure that characterizes the relationship of each neuron to a larger population, explaining seemingly complex network firing patterns in terms of basic circuit variables.

Are visual peripheries forever young?

The paper presents a concept of lifelong plasticity of peripheral vision. Central vision processing is accepted as critical and irreplaceable for normal perception in humans. While peripheral processing chiefly carries information about motion stimuli features and redirects foveal attention to new objects, it can also take over functions typical for central vision. Here I review the data showing the plasticity of peripheral vision found in functional, developmental, and comparative studies. Even though it is well established that afferent projections from central and peripheral retinal regions are not established simultaneously during early postnatal life, central vision is commonly used as a general model of development of the visual system. Based on clinical studies and visually deprived animal models, I describe how central and peripheral visual field representations separately rely on early visual experience. Peripheral visual processing (motion) is more affected by binocular visual deprivation than central visual processing (spatial resolution). In addition, our own experimental findings show the possible recruitment of coarse peripheral vision for fine spatial analysis. Accordingly, I hypothesize that the balance between central and peripheral visual processing, established in the course of development, is susceptible to plastic adaptations during the entire life span, with peripheral vision capable of taking over central processing.

Columnar architecture improves noise robustness in a model cortical network

Cortical columnar architecture was discovered decades ago yet there is no agreed upon explanation for its function. Indeed, some have suggested that it has no function, it is simply an epiphenomenon of developmental processes. To investigate this problem we have constructed a computer model of one square millimeter of layer 2/3 of the primary visual cortex (V1) of the cat. Model cells are connected according to data from recent paired cell studies, in particular the connection probability between pyramidal cells is inversely proportional both to the distance separating the cells and to the distance between the preferred parameters (features) of the cells. We find that these constraints, together with a columnar architecture, produce more tightly clustered populations of cells when compared to the random architecture seen in, for example, rodents. This causes the columnar network to converge more quickly and accurately on the pattern representing a particular stimulus in the presence of noise, suggesting that columnar connectivity functions to improve pattern recognition in cortical circuits. The model also suggests that synaptic failure, a phenomenon exhibited by weak synapses, may conserve metabolic resources by reducing transmitter release at these connections that do not contribute to network function.

Neural population coding: combining insights from microscopic and mass signals

Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior.

Neurons in cat V1 show significant clustering by degree of tuning

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons recorded at a single site were significantly more similar in each of these properties than pairs of neurons from different recording sites. The strength of the clustering was generally modest. The percent decrease in the median difference between pairs from the same site, relative to pairs from different sites, was as follows: for different measures of orientation tuning width, 29-35% (drifting gratings) or 15-25% (flashed gratings); for DSI, 24%; and for spatial frequency tuning width measured in octaves, 8% (drifting gratings). The clusterings of all of these measures were much weaker than for preferred orientation (68% decrease) but comparable to that seen for preferred spatial frequency in response to drifting gratings (26%). For the above properties, little difference in clustering was seen between simple and complex cells. In studies of spatial frequency tuning to flashed gratings, strong clustering was seen among simple-cell pairs for tuning width (70% decrease) and preferred frequency (71% decrease), whereas no clustering was seen for simple-complex or complex-complex cell pairs.

Superficial layer pyramidal cells communicate heterogeneously between multiple functional domains of cat primary visual cortex

The axons of pyramidal neurons in the superficial layers of the neocortex of higher mammals form lateral networks of discrete clusters of synaptic boutons. In primary visual cortex the clusters are reported to link domains that share the same orientation preferences, but how individual neurons contribute to this network is unknown. Here we performed optical imaging to record the intrinsic signal, which is an indirect measure of neuronal firing, and determined the global map of orientation preferences in the cat primary visual system. In the same experiment, single cells were recorded and labelled intracellularly. We found that individual axons arborise within the retinotopic representation of the classical receptive field, but their bouton clusters were not aligned along their preferred axis of orientation along the retinotopic map. Axon clusters formed in a variety of different orientation domains, not just the like-orientation domains. This topography and heterogeneity of single-cell connectivity provides circuits for normalization and context-dependent feature processing of visual scenes.

Zif268 mRNA Expression Patterns Reveal a Distinct Impact of Early Pattern Vision Deprivation on the Development of Primary Visual Cortical Areas in the Cat

Pattern vision deprivation (BD) can induce permanent deficits in global motion perception. The impact of timing and duration of BD on the maturation of the central and peripheral visual field representations in cat primary visual areas 17 and 18 remains unknown. We compared early BD, from eye opening for 2, 4, or 6 months, with late onset BD, after 2 months of normal vision, using the expression pattern of the visually driven activity reporter gene zif268 as readout. Decreasing zif268 mRNA levels between months 2 and 4 characterized the normal maturation of the (supra)granular layers of the central and peripheral visual field representations in areas 17 and 18. In general, all BD conditions had higher than normal zif268 levels. In area 17, early BD induced a delayed decrease, beginning later in peripheral than in central area 17. In contrast, the decrease occurred between months 2 and 4 throughout area 18. Lack of pattern vision stimulation during the first 4 months of life therefore has a different impact on the development of areas 17 and 18. A high zif268 expression level at a time when normal vision is restored seems to predict the capacity of a visual area to compensate for BD.

The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing

Sensory computations performed in the neocortex involve layer six (L6) cortico-cortical (CC) and cortico-thalamic (CT) signaling pathways. Developing an understanding of the physiological role of these circuits requires dissection of the functional specificity and connectivity of the underlying individual projection neurons. By combining whole-cell recording from identified L6 principal cells in the mouse primary visual cortex (V1) with modified rabies virus-based input mapping, we have determined the sensory response properties and upstream monosynaptic connectivity of cells mediating the CC or CT pathway. We show that CC-projecting cells encompass a broad spectrum of selectivity to stimulus orientation and are predominantly innervated by deep layer V1 neurons. In contrast, CT-projecting cells are ultrasparse firing, exquisitely tuned to orientation and direction information, and receive long-range input from higher cortical areas. This segregation in function and connectivity indicates that L6 microcircuits route specific contextual and stimulus-related information within and outside the cortical network.

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L6 cortico-cortical neurons are broadly tuned to stimulus orientation

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L6 cortico-thalamic neurons are sparse firing and highly tuned to stimulus orientation

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L6 cortico-cortical neurons receive input from cells located mostly within V1

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L6 cortico-thalamic neurons receive input from higher order cortical areas

Development and matching of binocular orientation preference in mouse V1

Eye-specific thalamic inputs converge in the primary visual cortex (V1) and form the basis of binocular vision. For normal binocular perceptions, such as depth and stereopsis, binocularly matched orientation preference between the two eyes is required. A critical period of binocular matching of orientation preference in mice during normal development is reported in literature. Using a reaction diffusion model we present the development of RF and orientation selectivity in mouse V1 and investigate the binocular orientation preference matching during the critical period. At the onset of the critical period the preferred orientations of the modeled cells are mostly mismatched in the two eyes and the mismatch decreases and reaches levels reported in juvenile mouse by the end of the critical period. At the end of critical period 39% of cells in binocular zone in our model cortex is orientation selective. In literature around 40% cortical cells are reported as orientation selective in mouse V1. The starting and the closing time for critical period determine the orientation preference alignment between the two eyes and orientation tuning in cortical cells. The absence of near neighbor interaction among cortical cells during the development of thalamo-cortical wiring causes a salt and pepper organization in the orientation preference map in mice. It also results in much lower % of orientation selective cells in mice as compared to ferrets and cats having organized orientation maps with pinwheels.

Local versus global scales of organization in auditory cortex

Topographic organization is a hallmark of sensory cortical organization. Topography is robust at spatial scales ranging from hundreds of microns to centimeters, but can dissolve at the level of neighboring neurons or subcellular compartments within a neuron. This dichotomous spatial organization is especially pronounced in the mouse auditory cortex, where an orderly tonotopic map can arise from heterogeneous frequency tuning between local neurons. Here, we address a debate surrounding the robustness of tonotopic organization in the auditory cortex that has persisted in some form for over 40 years. Drawing from various cortical areas, cortical layers, recording methodologies, and species, we describe how auditory cortical circuitry can simultaneously support a globally systematic, yet locally heterogeneous representation of this fundamental sound property.

Anatomical constraints on lateral competition in columnar cortical architectures

Competition is a well-studied and powerful mechanism for information processing in neuronal networks, providing noise rejection, signal restoration, decision making and associative memory properties, with relatively simple requirements for network architecture. Models based on competitive interactions have been used to describe the shaping of functional properties in visual cortex, as well as the development of functional maps in columnar cortex. These models require competition within a cortical area to occur on a wider spatial scale than cooperation, usually implemented by lateral inhibitory connections having a longer range than local excitatory connections. However, measurements of cortical anatomy reveal that the spatial extent of inhibition is in fact more restricted than that of excitation. Relatively few models reflect this, and it is unknown whether lateral competition can occur in cortical-like networks that have a realistic spatial relationship between excitation and inhibition. Here we analyze simple models for cortical columns and perform simulations of larger models to show how the spatial scales of excitation and inhibition can interact to produce competition through disynaptic inhibition. Our findings give strong support to the direct coupling effect-that the presence of competition across the cortical surface is predicted well by the anatomy of direct excitatory and inhibitory coupling and that multisynaptic network effects are negligible. This implies that for networks with short-range inhibition and longer-range excitation, the spatial extent of competition is even narrower than the range of inhibitory connections. Our results suggest the presence of network mechanisms that focus on intra-rather than intercolumn competition in neocortex, highlighting the need for both new models and direct experimental characterizations of lateral inhibition and competition in columnar cortex.

Natural image sequences constrain dynamic receptive fields and imply a sparse code

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input.

Neural maps versus salt-and-pepper organization in visual cortex

Theoretical neuroscientists have long been intrigued by the spatial patterns of neuronal selectivities observed in the visual cortices of many mammals, including primates. While theoretical studies have contributed significantly to our understanding of how the brain learns to see, recent experimental discoveries of the spatial irregularity of visual response properties in the rodent visual cortex have prompted new questions about the origin and functional significance of cortical maps. Characterizing the marked differences of cortical design principles among species and comparing them may provide us with a deeper understanding of primate and non-primate vision.