Search

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico–cortical connectivity. However, the evolutionary origin of these changes in connectivity and how they affected cortical circuit function and behaviour are currently unknown. The human-specific gene duplication SRGAP2C emerged in the ancestral genome of the Homo lineage before the major phase of increase in brain size1,2. SRGAP2C expression in mice increases the density of excitatory and inhibitory synapses received by layer 2/3 pyramidal neurons (PNs)3–5. Here we show that the increased number of excitatory synapses received by layer 2/3 PNs induced by SRGAP2C expression originates from a specific increase in local and long-range cortico–cortical connections. Mice humanized for SRGAP2C expression in all cortical PNs displayed a shift in the fraction of layer 2/3 PNs activated by sensory stimulation and an enhanced ability to learn a cortex-dependent sensory-discrimination task. Computational modelling revealed that the increased layer 4 to layer 2/3 connectivity induced by SRGAP2C expression explains some of the key changes in sensory coding properties. These results suggest that the emergence of SRGAP2C at the birth of the Homo lineage contributed to the evolution of specific structural and functional features of cortical circuits in the human cortex. The human-specific gene duplication SRGAP2C is identified as a modifier of structural and functional features of cortical circuits leading to improved behavioural performance that may have allowed the emergence of cognitive properties characterizing the human brain.

1AbstractA cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon – whether behavioral or a pattern of neural activity – and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

The cortical microcircuit can dynamically adjust to dramatic changes in the strength, scale, and complexity of its input. In the primary visual cortex (V1), pyramidal cells (PCs) integrate widely across space when signals are weak, but narrowly when signals are strong, a phenomenon known as contrast-dependent surround suppression. Theoretical work has proposed that local interneurons could mediate a shift from cooperation to competition of PCs across cortical space, underlying this computation. We combined calcium imaging and electrophysiology to constrain a stabilized supralinear network model that explains how the four principal cell types in layer 2/3 (L2/3) of mouse V1– somatostatin (SST), parvalbumin (PV), and vasoactive intestinal peptide (VIP) interneurons, and PCs– transform inputs from layer 4 (L4) PCs to encode drifting gratings of varying size and contrast. Using bidirectional optogenetic perturbations, we confirmed key predictions of the model. Our data and modeling showed that recurrent amplification drives a transition from a positive PC→VIP⊣SST⊣PC feedback loop at small size and low contrast to a negative PC→SST⊣PC feedback loop at large size and high contrast to contribute to this flexible computation. This may represent a widespread mechanism for gating competition across cortical space to optimally meet task demands.

Electrophysiological recording in the primary visual cortex (V1) of mammals have revealed a number of complex interactions between the center and surround. Understanding the underlying circuit mechanisms is crucial to understanding fundamental brain computations. In this paper we address the following phenomena that have been observed in V1 of animals with orientation maps: 1) surround suppression that is accompanied by a decrease in the excitatory and inhibitory currents that the cell receives as the stimulus size increases beyond the cell’s summation field; 2) surround tuning to the center orientation, in which the strongest suppression arises when the surround orientation matches that of the center stimulus; and 3) feature-specific suppression, in which a surround stimulus of a given orientation specifically suppresses that orientation’s component of the response to a center plaid stimulus. We show that a stabilized supralinear network that has biologically plausible connectivity and synaptic efficacies that depend on cortical distance and orientation difference between neurons can consistently reproduce all the above phenomena. We explain the mechanism behind each result, and argue that feature-specific suppression and surround tuning to the center orientation are independent phenomena. Specifically, if we remove some aspects of the connectivity from the model it will still produce feature-specific suppression but not surround tuning to the center orientation. We also show that in the model the activity decay time constant is similar to the cortical activity decay time constant reported in mouse V1. Our model indicates that if the surround activates neurons that fall within the reach of the horizontal projections in V1, the above mentioned phenomena can be generated by V1 alone without the need of cortico-cortical feedback. Finally, we show that these results hold both in networks with rate-based units and with conductance-based spiking units. This demonstrates that the stabilized supra-linear network mechanism can be achieved in the more biological context of spiking networks.

Predicting the response of the cortical microcircuit to perturbations is a prerequisite to determine the mechanisms that mediate its response to stimulus; yet, an encompassing perspective that describes the full ensemble of the network’s response in models that accurately recapitulate recorded data is still lacking. Here we develop a class of mathematically tractable models that exactly describe the modulation of the distribution of cell-type-specific calcium-imaging activity with the contrast of a visual stimulus. The inferred parameters recover signatures of the connectivity structure found in mouse visual cortex. Analysis of this structure subsequently reveals parameter-independent relations between the responses of different cell types to perturbations and each interneuron’s role in circuit-stabilization. Leveraging recent theoretical approaches, we derive explicit expressions for the distribution of responses to partial perturbations which reveal a novel, counter intuitive effect in the sign of response functions. Finally applying the theory to inferring feedback to V1 during locomotion, we find that it is predominantly mediated by both SOM and VIP modulation.

An inhibition-stabilized network (ISN) is a network of excitatory and inhibitory cells at a stable fixed point of firing rates for a given input, for which the excitatory subnetwork would be unstable if inhibitory rates were frozen at their fixed point values. It has been shown that in a low-dimensional model (one unit per neuronal subtype) of an ISN with a single excitatory and single inhibitory cell type, the inhibitory unit shows a “paradoxical” response, lowering (raising) its steady-state firing rate in response to addition to it of excitatory (inhibitory) input. This has been generalized to an ISN with multiple inhibitory cell types: if input is given only to inhibitory cells, the steady-state inhibition received by excitatory cells changes paradoxically, that is, it decreases (increases) if the steady-state excitatory firing rates decrease (increase).We generalize these analyses of paradoxical effects to low-dimensional networks with multiple cell types of both excitatory and inhibitory neurons. The analysis depends on the connectivity matrix of the network linearized about a given fixed point, and its eigenvectors or “modes”. We show the following: (1) A given cell type shows a paradoxical change in steady-state rate in response to input it receives, if and only if the network with that cell type omitted has an odd number of unstable modes. Excitatory neurons can show paradoxical responses when there are two or more inhibitory subtypes. (2) More generally, if the cell types are divided into two nonoverlapping subsets A and B, then subset B has an odd (even) number of modes that show paradoxical response if and only if subset A has an odd (even) number of unstable modes. (3) The net steady-state inhibition received by any unstable mode of the excitatory subnetwork will change paradoxically, i.e. in the same direction as the change in amplitude of that mode. In particular, this means that a sufficient condition to determine that a network is an ISN is if, in response to an input only to inhibitory cells, the firing rates of and inhibition received by all excitatory cell types all change in the same direction. This in turn will be true if all E cells and all inhibitory cell types that connect to E cells change their firing rates in the same direction.

Context guides perception by influencing stimulus saliency. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The underlying mechanisms remain unclear. Here, we use optical recordings, manipulations, and computational modeling to show that disinhibitory circuits consisting of vasoactive intestinal peptide (VIP)-expressing and somatostatin (SOM)-expressing inhibitory neurons modulate responses in mouse visual cortex depending on similarity between stimulus and surround, primarily by modulating recurrent excitation. When stimulus and surround are similar, VIP neurons are inactive, and activity of SOM neurons leads to suppression of excitatory neurons. However, when stimulus and surround differ, VIP neurons are active, inhibiting SOM neurons, which leads to relief of excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit that contributes to contextual modulation and may regulate perceptual saliency.

In the visual system, the response to a stimulus in a neuron’s receptive field can be modulated by stimulus context, and the strength of these contextual influences vary with stimulus intensity. Recent work has shown how a theoretical model, the stabilized supralinear network (SSN), can account for such modulatory influences, using a small set of computational mechanisms. While the predictions of the SSN have been confirmed in primary visual cortex (V1), its computational principles apply with equal validity to any cortical structure. We have therefore tested the generality of the SSN by examining modulatory influences in the middle temporal area (MT) of the macaque visual cortex, using electrophysiological recordings and pharmacological manipulations. We developed a novel stimulus that can be adjusted parametrically to be larger or smaller in the space of all possible motion directions. We found, as predicted by the SSN, that MT neurons integrate across motion directions for low-contrast stimuli, but that they exhibit suppression by the same stimuli when they are high in contrast. These results are analogous to those found in visual cortex when stimulus size is varied in the space domain. We further tested the mechanisms of inhibition using pharmacologically manipulations of inhibitory efficacy. As predicted by the SSN, local manipulation of inhibitory strength altered firing rates, but did not change the strength of surround suppression. These results are consistent with the idea that the SSN can account for modulatory influences along different stimulus dimensions and in different cortical areas.Significance StatementVisual neurons are selective for specific stimulus features in a region of visual space known as the receptive field, but can be modulated by stimuli outside of the receptive field. The SSN model has been proposed to account for these and other modulatory influences, and tested in V1. As this model is not specific to any particular stimulus feature or brain region, we wondered whether similar modulatory influences might be observed for other stimulus dimensions and other regions. We tested for specific patterns of modulatory influences in the domain of motion direction, using electrophysiological recordings from MT. Our data confirm the predictions of the SSN in MT, suggesting that the SSN computations might be a generic feature of sensory cortex.

SO_SCPLOWUMMARYC_SCPLOWContext strongly guides perception by influencing the saliency of sensory stimuli. Accordingly, sensory neurons respond differently to the same stimulus depending on the context in which the stimulus is embedded. In the visual system, the relationship between the features of the stimulus and those of the surround determine how excitatory neurons are modulated. Their responses are suppressed when center and surround share the same features but not when they are different. The mechanisms that relieve surround suppression and thereby generate contextual modulation remain unclear. Here we show that the disinhibitory circuit consisting of somatostatin-expressing (SOM) and vasoactive-intestinal-peptide-expressing (VIP) inhibitory neurons contextually modulates excitatory neurons in mouse primary visual cortex. When the stimulus and the surround share the same features, VIP neurons are inactive and SOM neurons can suppress excitatory neurons. However, when the features of the stimulus differ from those of the surround, VIP neurons are activated, thereby inhibiting SOM neurons and relieving excitatory neurons from suppression. Optogenetic silencing of VIP neurons, via disinhibition of SOM neurons, reduces the contextual modulation of excitatory neurons. We have identified a canonical cortical disinhibitory circuit which contributes to contextual modulation of excitatory neurons, and may thereby regulate perceptual saliency.

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In the case of artificial neural networks, the three components specified by design are the objective functions, the learning rules, and architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size to the factors that cancel, rather than tight, meaning that the net input is very small relative to the cancelling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas., 21 pages, 2 figures

One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.

Physical features of sensory stimuli are fixed, but sensory perception is context dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones and performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed during behavior, while some cells became more active. Whole-cell recordings showed that excitatory inputs were affected only modestly by context, but inhibition was more sensitive, with PV+, SOM+, and VIP+ interneurons balancing inhibition and disinhibition within the network. Cholinergic modulation was involved in context switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior.

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives., eLife digest Imagine you have lost your cell phone. Your eyes scan the cluttered table in front of you, searching for its familiar blue case. But what is happening within the visual areas of your brain while you search? One possibility is that neurons that represent relevant features such as 'blue' and 'rectangular' increase their activity. This might help you spot your phone among all the other objects on the table. Paying attention to specific features improves our performance on visual tasks that require detecting those features. The 'feature similarity gain model' proposes that this is because attention increases the activity of neurons sensitive to specific target features, such as ‘blue’ in the example above. But is this how the brain solves such challenges in practice? Previous studies examining this issue have relied on correlations. They have shown that increases in neural activity correlate with improved performance on visual tasks. But correlation does not imply causation. Lindsay and Miller have now used a computer model of the brain’s visual pathway to examine whether changes in neural activity cause improved performance. The model was trained to use feature similarity gain to detect an object within a set of photographs. As predicted, changes in activity like those that occur in the brain did indeed improve the model’s performance. Moreover, activity changes at later stages of the model's processing pathway produced bigger improvements than activity changes earlier in the pathway. This may explain why attention affects neural activity more at later stages in the visual pathway. But feature similarity gain is not the only possible explanation for the results. Lindsay and Miller show that another pattern of activity change also enhanced the model’s performance, and propose an experiment to distinguish between the two possibilities. Overall, these findings increase our understanding of how the brain processes sensory information. Work is ongoing to teach computers to process images as efficiently as the human visual system. The computer model used in this study is similar to those used in state-of-the-art computer vision. These findings could thus help advance artificial sensory processing too.

Neurons in sensory cortex integrate multiple influences to parse objects and support perception. Across multiple cortical areas, integration is characterized by two neuronal response properties: (1) surround suppression: modulatory contextual stimuli suppress responses to driving stimuli; (2) “normalization”: responses to multiple driving stimuli add sublinearly. These properties depend on input strength: for weak driving stimuli, contextual influences more weakly suppress or facilitate and summation becomes linear or supralinear. Understanding the circuit operations underlying integration is critical to understanding cortical function and disease. We present a simple, general theory. A wealth of integrative properties including the above emerge robustly from four properties of cortical circuitry: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; (4) simple spatial properties of intracortical connections. Integrative properties emerge dynamically as circuit properties, with excitatory and inhibitory neurons showing similar behaviors. In new recordings in visual cortex, we confirm key model predictions.

Of all of the sensory areas, barrel cortex is among the best understood in terms of circuitry, yet least understood in terms of sensory function. We combined intracellular recording in rats with a multi-directional, multi-whisker stimulator system to estimate receptive fields by reverse correlation of stimuli to synaptic inputs. Spatiotemporal receptive fields were identified orders of magnitude faster than by conventional spike-based approaches, even for neurons with little spiking activity. Given a suitable stimulus representation, a linear model captured the stimulus-response relationship for all neurons with high accuracy. In contrast with conventional single-whisker stimuli, complex stimuli revealed markedly sharpened receptive fields, largely as a result of adaptation. This phenomenon allowed the surround to facilitate rather than to suppress responses to the principal whisker. Optimized stimuli enhanced firing in layers 4-6, but not in layers 2/3, which remained sparsely active. Surround facilitation through adaptation may be required for discriminating complex shapes and textures during natural sensing.

What causes critical periods (CPs) to open? For the best-studied case, ocular dominance plasticity in primary visual cortex in response to monocular deprivation (MD), the maturation of inhibition is necessary and sufficient. How does inhibition open the CP? We present a novel theory: the transition from pre-CP to CP plasticity arises because inhibition preferentially suppresses responses to spontaneous relative to visually-driven input activity, switching learning cues from internal to external sources. This differs from previous proposals in (1) arguing that the CP can open without changes in plasticity mechanisms when, through circuit development, activity patterns become more sensitive to sensory experience; (2) explaining not simply a transition from no plasticity to plasticity, but rather the change in outcome of MD-induced plasticity,from pre-CP to CP,. More broadly, hierarchical organization of sensory-motor pathways may develop through a cascade of CPs induced as circuit maturation progresses from “lower” to “higher” cortical areas.

SummaryVariability and correlations in cortical activity are ubiquitously modulated by stimuli. Correlated variability is quenched following stimulus onset across multiple cortical areas, suppressing low-frequency components of the LFP and of Vm-LFP coherence. Modulation of Fano factors and correlations in area MT is tuned for stimulus direction. What circuit mechanisms underly these behaviors? We show that a simple model circuit, the stochastic Stabilized Supralinear Network (SSN), robustly explains these results. Stimuli modulate variability by modifying two forms of effective connectivity between activity patterns that characterize excitatory-inhibitory (E/I) circuits. Increases in the strength with which activity patterns inhibit themselves reduce correlated variability, while increases in feedforward connections between patterns (transforming E/I imbalance into balanced fluctuations) increase variability. These results suggest an operating regime of cortical dynamics that involves fast fluctuations and fast responses to stimulus changes, unlike previous models of variability suppression through suppression of chaos or networks with multiple attractors.

Early in development, the cat primary visual cortex (V1) is dominated by inputs driven by the contralateral eye. The pattern then reorganizes into ocular dominance columns that are roughly equally distributed between inputs serving the two eyes. This reorganization does not occur if the eyes are kept closed. The mechanism of this equalization is unknown. It has been argued that it is unlikely to involve Hebbian activity-dependent learning rules, on the assumption that these would favor an initially dominant eye. The reorganization occurs at the onset of the critical period (CP) for monocular deprivation (MD), the period when MD can cause a shift of cortical innervation in favor of the nondeprived eye. In mice, the CP is opened by the maturation of cortical inhibition, which does not occur if the eyes are kept closed. Here we show how these observations can be united: under Hebbian rules of activity-dependent synaptic modification, strengthening of intracortical inhibition can lead to equalization of the two eyes' inputs. Furthermore, when the effects of homeostatic synaptic plasticity or certain other mechanisms are incorporated, activity-dependent learning can also explain how MD causes a shift toward the open eye during the CP despite the drive by inhibition toward equalization of the two eyes' inputs. Thus, assuming similar mechanisms underlie the onset of the CP in cats as in mice, this and activity-dependent learning rules can explain the interocular equalization observed in cat V1 and its failure to occur without visual experience.

SummaryIn what regime does the cortical circuit operate? Our intracellular studies of surround suppression in cat primary visual cortex (V1) provide strong evidence on this question. Although suppression has been thought to arise from an increase in lateral inhibition, we find that the inhibition that cells receive is reduced, not increased, by a surround stimulus. Instead, suppression is mediated by a withdrawal of excitation. Thalamic recordings and previous work show that these effects cannot be explained by a withdrawal of thalamic input. We find in theoretical work that this behavior can only arise if V1 operates as an inhibition-stabilized network (ISN), in which excitatory recurrence alone is strong enough to destabilize visual responses but feedback inhibition maintains stability. We confirm two strong tests of this scenario experimentally and show through simulation that observed cell-to-cell variability in surround effects, from facilitation to suppression, can arise naturally from variability in the ISN.

SummaryIn cerebral cortex, ongoing activity absent a stimulus can resemble stimulus-driven activity in size and structure. In particular, spontaneous activity in cat primary visual cortex (V1) has structure significantly correlated with evoked responses to oriented stimuli. This suggests that, from unstructured input, cortical circuits selectively amplify specific activity patterns. Current understanding of selective amplification involves elongation of a neural assembly's lifetime by mutual excitation among its neurons. We introduce a new mechanism for selective amplification without elongation of lifetime: “balanced amplification.” Strong balanced amplification arises when feedback inhibition stabilizes strong recurrent excitation, a pattern likely to be typical of cortex. Thus, balanced amplification should ubiquitously contribute to cortical activity. Balanced amplification depends on the fact that individual neurons project only excitatory or only inhibitory synapses. This leads to a hidden feedforward connectivity between activity patterns. We show in a detailed biophysical model that this can explain the cat V1 observations.

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical input largely, but not completely, cancels this external input. This operation leads to many characteristic cortical nonlinearities in integrating multiple stimuli. The cortical computation must combine such local processing with hierarchical processing across areas. We point to important changes in moving from sensory cortex to motor and frontal cortex and the possibility of substantial differences between cortex in rodents vs. species with columnar organization of selectivity.

Little is known about the internal circuitry of the primate lateral intraparietal area (LIP). During two versions of a delayed-saccade task, we found radically different network dynamics beneath similar population average firing patterns. When neurons are not influenced by stimuli outside their receptive fields (RFs), dynamics of the high-dimensional LIP network during slowly varying activity lie predominantly in one multi-neuronal dimension, as described previously. However, when activity is suppressed by stimuli outside the RF, slow LIP dynamics markedly deviate from a single dimension. The conflicting results can be reconciled if two LIP local networks, each underlying an RF location and dominated by a single multi-neuronal activity pattern, are suppressively coupled to each other. These results demonstrate the low dimensionality of slow LIP local dynamics, and suggest that LIP local networks encoding the attentional and movement priority of competing visual locations actively suppress one another.

Previously, we proposed a model of the circuitry underlying simple-cell responses in cat primary visual cortex (V1) layer 4. We argued that the ordered arrangement of lateral geniculate nucleus inputs to a simple cell must be supplemented by a component of feedforward inhibition that is untuned for orientation and responds to high temporal frequencies to explain the sharp contrast-invariant orientation tuning and low-pass temporal frequency tuning of simple cells. The temporal tuning also requires a significant NMDA component in geniculocortical synapses. Recent experiments have revealed cat V1 layer 4 inhibitory neurons with two distinct types of receptive fields (RFs): complex RFs with mixed ON/OFF responses lacking in orientation tuning, and simple RFs with normal, sharp-orientation tuning (although, some respond to all orientations). We show that complex inhibitory neurons can provide the inhibition needed to explain simple-cell response properties. Given this complex cell inhibition, antiphase or “push-pull” inhibition from tuned simple inhibitory neurons acts to sharpen spatial frequency tuning, lower responses to low temporal frequency stimuli, and increase the stability of cortical activity.

This paper reviews theoretical and experimental results on the processing of layer 4, the input-recipient layer, of cat primary visual cortex (V1). A wide range of experimental data can be understood from a model in which response tuning of layer 4 cells is largely determined by a local interplay of feedforward excitation (from thalamus) and feedforward inhibition (from layer 4 inhibitory interneurons driven by thalamus). Feedforward inhibition dominates excitation, inherits its tuning from the thalamic input and sharpens the tuning of excitatory cells. At least a strong component of the feedforward inhibition received by a cell is spatially opponent to the excitation it receives, meaning that inhibition is driven by dark in regions of the visual field in which excitation is driven by light, and vice versa. The idea of opponent inhibition can be generalized to mean inhibition driven by input patterns that are strongly anti-correlated with the patterns that excite a cell. This paper argues that dominant feedforward opponent inhibition may be a general principle of cortical layer 4. This leads to the suggestion that the properties that show columnar organization--invariance across the vertical depth of cortex--may be properties that are shared by 'opposite' (anticorrelated) stimulus pairs. This contrasts with the more common idea that a column represents a set of cells that all share similar stimulus preferences.

We model the development of the functional circuit of layer 4 (the input-recipient layer) of cat primary visual cortex. The observed thalamocortical and intracortical circuitry codevelop under Hebb-like synaptic plasticity. Hebbian development yields opponent inhibition: inhibition evoked by stimuli anticorrelated with those that excite a cell. Strong opponent inhibition enables recognition of stimulus orientation in a manner invariant to stimulus contrast. These principles may apply to cortex more generally: Hebb-like plasticity can guide layer 4 of any piece of cortex to create opposition between anticorrelated stimulus pairs, and this enables recognition of specific stimulus patterns in a manner invariant to stimulus magnitude. Properties that are invariant across a cortical column are predicted to be those shared by opponent stimulus pairs; this contrasts with the common idea that a column represents cells with similar response properties.

A central theme in neural coding concerns the role of response variability and noise in determining the information transmission of neurons. This issue was investigated in single cells of the lateral geniculate nucleus of barbiturate-anesthetized cats by quantifying the degree of precision in and the information transmission properties of individual spike train responses to full field, binary (bright or dark), flashing stimuli. We found that neuronal responses could be highly reproducible in their spike timing (approximately 1-2 ms standard deviation) and spike count (approximately 0.3 ratio of variance/mean, compared with 1.0 expected for a Poisson process). This degree of precision only became apparent when an adequate length of the stimulus sequence was specified to determine the neural response, emphasizing that the variables relevant to a cell's response must be controlled to observe the cell's intrinsic response precision. Responses could carry as much as 3.5 bits/spike of information about the stimulus, a rate that was within a factor of two of the limit the spike train could transmit. Moreover, there appeared to be little sign of redundancy in coding: on average, longer response sequences carried at least as much information about the stimulus as would be obtained by adding together the information carried by shorter response sequences considered independently. There also was no direct evidence found for synergy between response sequences. These results could largely, but not entirely, be explained by a simple model of the response in which one filters the stimulus by the cell's impulse response kernel, thresholds the result at a fairly high level, and incorporates a postspike refractory period.

Mature visual cortex shows a single, binocularly matched orientation map. This matching develops without visual experience. It persists despite early monocular deprivation that largely eliminates one eye's map, followed by reverse suture (deprivation of the previously open eye and opening of the previously deprived eye), even though the two eyes lack common visual experience in this case. These results have been interpreted to suggest that the structure of orientation maps either is innately predetermined or, if it arises through self-organization, is determined by external cues such as boundary conditions or a “scaffolding” of horizontal connections. We show, to the contrary, that these results are the expected outcomes if orientation maps develop through activity-instructed, correlation-based development of the geniculocortical connections without additional cues. A weak, binocularly correlated orientation map is known to exist before deprivation onset; we previously showed how this can arise through activity-instructed development. Now we show that this initial correlation between the two eyes' maps can persist or increase despite deprivation sufficient to cause massive loss of the deprived eye's geniculocortical synaptic strength, followed by reverse suture. Given sufficient early correlated map development, each map's fate is “dynamically committed”: the two eyes' maps will converge upon a common outcome, even if developing independently. This dynamic fate commitment is retained even after severe deprivation.

In cortical simple cells of cat striate cortex, the response to a visual stimulus of the preferred orientation is partially suppressed by simultaneous presentation of a stimulus at the orthogonal orientation, an effect known as “cross-orientation inhibition”. It has been argued that this is due to the presence of inhibitory connections between cells tuned for different orientations, but intracellular studies suggest that simple cells only receive inhibitory input from cells with similar orientation tuning. Here we present a model, based on local intracortical connectivity between cells of similar orientation tuning, that can account for many aspects of cross-orientation inhibition.

There are several response properties that distinguish cortical simple cells of cat V1 layer 4 from the geniculate cells that provide their input. We have recently demonstrated that a developmentally motivated pattern of intracortical inhibition, in conjunction with the observed pattern of geniculocortical connections, can robustly account for the contrast-invariance of simple cell orientation tuning. We now show that this same model circuit unexpectedly accounts for the low-pass shift in the temporal frequency tuning curves of cortical cells relative to geniculate cells. This arises when NMDA-mediated excitatory conductances are included in the thalamocortical synapses, at levels observed experimentally.

Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.

Partially isolated cortical islands prepared in vivo become epileptogenic within weeks of the injury. In this model of chronic epileptogenesis, recordings from cortical slices cut through the injured area and maintained in vitro often show evoked, long- and variable-latency multiphasic epileptiform field potentials that also can occur spontaneously. These events are initiated in layer V and are synchronous with polyphasic long-duration excitatory and inhibitory potentials (currents) in neurons that may last several hundred milliseconds. Stimuli that are significantly above threshold for triggering these epileptiform events evoke only a single large excitatory postsynaptic potential (EPSP) followed by an inhibitory postsynaptic potential (IPSP). We investigated the physiological basis of these events using simulations of a layer V network consisting of 500 compartmental model neurons, including 400 principal (excitatory) and 100 inhibitory cells. Epileptiform events occurred in response to a stimulus when sufficient N-methyl-d-aspartate (NMDA) conductance was activated by feedback excitatory activity among pyramidal cells. In control simulations, this activity was prevented by the rapid development of IPSPs. One manipulation that could give rise to epileptogenesis was an increase in the threshold of inhibitory interneurons. However, previous experimental data from layer V pyramidal neurons of these chronic epileptogenic lesions indicate: upregulation, rather than downregulation, of inhibition; alterations in the intrinsic properties of pyramidal cells that would tend to make them more excitable; and sprouting of their intracortical axons and increased numbers of presumed synaptic contacts, which would increase recurrent EPSPs from one cell onto another. Consistent with this, we found that increasing the excitability of pyramidal cells and the strength of NMDA conductances, in the face of either unaltered or increased inhibition, resulted in generation of epileptiform activity that had characteristics similar to those of the experimental data. Thus epileptogenesis such as occurs after chronic cortical injury can result from alterations of intrinsic membrane properties of pyramidal neurons together with enhanced NMDA synaptic conductances.

We examine the effects of short-term synaptic depression on the orientation tuning of the LGN input to simple cells in cat primary visual cortex (V1). The total LGN input has an untuned component as well as a tuned component, both of which grow with stimulus contrast. The untuned component is not visible in the firing rate responses of the simple cells. The suppression of the contribution of the untuned input component to firing rate responses is key to establishing orientation selectivity and its invariance with stimulus contrast. It has been argued that synaptic depression of LGN inputs could contribute to the selective suppression of the untuned component and thus contribute to the tuning observed in simple cells. We examine this using a model fit to the depression observed at thalamocortical synapses in-vivo, and compare this to an earlier model fit based on in-vitro observations. We examine the tuning of both the conductance and the firing rate induced in simple cells by the net LGN input. We find that depression causes minimal suppression of the untuned component. The primary effect of depression is to cause the contrast response curve to saturate at lower contrasts without differentially affecting the tuned vs. untuned components. This effect is slightly weaker for in-vivo vs. in-vitro parameters. Thus, synaptic depression of LGN inputs does not appreciably contribute to the orientation tuning of V1 simple cells.

Neurons in the primary visual cortex of higher mammals respond selectively to light/dark borders of a particular orientation. The receptive fields of simple cells, a type of orientation-selective cell, consist of adjacent, oriented regions alternately receiving ON-center and OFF-center excitatory input. I show that this segregation of inputs within receptive fields can occur through an activity-dependent competition between ON-center and OFF-center inputs, just as segregation of inputs between different postsynaptic cells into ocular dominance columns appears to occur through activity-dependent competition between left-eye and right-eye inputs. These different outcomes are proposed to result, not from different mechanisms, but from different spatial structures of the correlations in neural activity among the competing inputs in each case. Simple cells result if ON-center inputs are best correlated with other ON-center inputs, and OFF with OFF, at small retinotopic separations, but ON-center inputs are best correlated with OFF-center inputs at larger separations. This hypothesis leads robustly to development of simple cell receptive fields selective for orientation and spatial frequency, and to the continuous and periodic arrangement of preferred orientation across the cortex. Input correlations determine the mean preferred spatial frequency and degree of orientation selectivity. Estimates of these correlations based on measurements in adult cat retina (Mastronarde, 1983a,b) produce quantitative predictions for the mean preferred spatial frequencies of cat simple cells across eccentricities that agree with experiments (Movshon et al., 1978b). Intracortical interactions are the primary determinant of cortical organization. Simple cell spatial phases can play a key role in this organization, so arrangements of spatial phases and preferred orientations may need to be studied together to understand either alone. Possible origins for other cortical features including spatial frequency clusters, afferent ON/OFF segregation, blobs, pinwheels, and opponent inhibition within simple cell receptive fields are suggested. A number of strong experimental tests of the hypothesis are proposed.

In cortical simple cells of cat striate cortex, the response to a visual stimulus of the preferred orientation is partially suppressed by simultaneous presentation of a stimulus at the orthogonal orientation, an effect known as “cross-orientation inhibition.” It has been argued that this is due to the presence of inhibitory connections between cells tuned for different orientations, but intracellular studies suggest that simple cells receive inhibitory input primarily from cells with similar orientation tuning. Furthermore, response suppression can be elicited by a variety of nonpreferred stimuli at all orientations. Here we study a model circuit that was presented previously to address many aspects of simple cell orientation tuning, which is based on local intracortical connectivity between cells of similar orientation tuning. We show that this model circuit can account for many aspects of cross-orientation inhibition and, more generally, of response suppression by nonpreferred stimuli and of other nonlinear properties of responses to stimulation with multiple gratings.

Cells in cerebral cortex fail to respond to fast-moving stimuli that evoke strong responses in the thalamic nuclei innervating the cortex. The reason for this behavior has remained a mystery. We study an experimentally motivated model of the thalamic input-recipient layer of cat primary visual cortex that accounts for many aspects of cortical orientation tuning. In this circuit, inhibition dominates over excitation, but temporal modulations of excitation and inhibition occur out of phase with one another, allowing excitation to transiently drive cells. We show that this circuit provides a natural explanation of cortical low-pass temporal frequency tuning, provided N-methyl-D-aspartate (NMDA) receptors are present in thalamocortical synapses in proportions measured experimentally. This suggests a new and unanticipated role for NMDA conductances in shaping the temporal response properties of cortical cells, and suggests that common cortical circuit mechanisms underlie both spatial and temporal response tuning.

The origin of orientation selectivity in the responses of simple cells in cat visual cortex serves as a model problem for understanding cortical circuitry and computation. The feed-forward model posits that this selectivity arises simply from the arrangement of thalamic inputs to a simple cell. Much evidence, including a number of recent intracellular studies, supports a primary role of the thalamic inputs in determining simple cell response properties, including orientation tuning. This mechanism alone, however, cannot explain the invariance of orientation tuning to changes in stimulus contrast. Simple cells receive push-pull inhibition: ON inhibition in OFF subregions and vice versa. Addition of such inhibition to the feed-forward model can account for this contrast invariance, provided the inhibition is sufficiently strong. The predictions of “normalization” and “feedback” models are reviewed and compared with the predictions of this modified feed-forward model and with experimental results. The modified feed-forward and the feedback models ascribe fundamentally different functions to cortical processing.

The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of lateral geniculate nucleus (LGN) afferents to layer 4 simple cells. However, this explanation is not sufficient to account for the contrast invariance of orientation tuning.To understand contrast invariance, we first characterize the input to cat simple cells generated by the oriented arrangement of LGN afferents. We demonstrate that it has two components: a spatial-phase-specific component (i.e., one that depends on receptive field spatial phase), which is tuned for orientation, and a phase-nonspecific component, which is untuned. Both components grow with contrast.Second, we show that a correlation-based intracortical circuit, in which connectivity between cell pairs is determined by the correlation of their LGN inputs, is sufficient to achieve well tuned, contrast-invariant orientation tuning. This circuit generates both spatially opponent, “antiphase” inhibition (“push–pull”), and spatially matched, “same-phase” excitation. The inhibition, if sufficiently strong, suppresses the untuned input component and sharpens responses to the tuned component at all contrasts. The excitation amplifies tuned responses. This circuit agrees with experimental evidence showing spatial opponency between, and similar orientation tuning of, the excitatory and inhibitory inputs received by a simple cell. Orientation tuning is primarily input driven, accounting for the observed invariance of tuning width after removal of intracortical synaptic input, as well as for the dependence of orientation tuning on stimulus spatial frequency.The model differs from previous push–pull models in requiring dominant rather than balanced inhibition and in predicting that a population of layer 4 inhibitory neurons should respond in a contrast-dependent manner to stimuli of all orientations, although their tuning width may be similar to that of excitatory neurons. The model demonstrates that fundamental response properties of cortical layer 4 can be explained by circuitry expected to develop under correlation-based rules of synaptic plasticity, and shows how such circuitry allows the cortex to distinguish stimulus intensity from stimulus form.

To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple integrate-and-fire model to the experimentally measured integrative properties of cortical regular spiking cells (McCormick, Connors, Lighthall, & Prince, 1985). After setting RC parameters, the postspike voltage reset is set to match experimental measurements of neuronal gain (obtained from in vitro plots of firing frequency versus injected current). Examination of the resulting model leads to an intuitive picture of neuronal integration that unifies the seemingly contradictory [Formula: see text] and random walk pictures that have previously been proposed. When ISIs are dominated by postspike recovery,[Formula: see text] arguments hold and spiking is regular; after the “memory” of the last spike becomes negligible, spike threshold crossing is caused by input variance around a steady state and spiking is Poisson. In integrate-and-fire neurons matched to cortical cell physiology, steady-state behavior is predominant, and ISIs are highly variable at all physiological firing rates and for a wide range of inhibitory and excitatory inputs.

What makes a useful model of neural development? One important contribution of modeling is to demonstrate that proposed biological mechanisms can be sufficient to account for experimental results. The Von der Malsburg model is a classic example. But such demonstrations alone do not provide tools to experimentally distinguish one mechanism from another. To draw such distinctions, the connection between measurable biological quantities and developmental outcomes must be established. Perhaps the most important task for the future of developmental modeling is to deepen the connection between theory and experiment. Experimentally, this requires detailed and difficult measurements or experimental perturbations of the correlations among inputs and the intracortical connectivity existing during development. Simultaneous measurement of the maps of spatial phase and orientation of mature simple cells will provide important information for the understanding of orientation column development. Theoretically, the number of open problems is enormous. How will inclusion of additional plasticity mechanisms, such as sprouting and retraction of synapses or plasticity of intracortical connections, alter the analytical understanding thus far achieved? What precisely determines the width of orientation columns in the model presented here? Can the relationship between ocular dominance and orientation columns be understood from developmental rules in a testable way? The existing framework may be extended to a three-dimensional cortex and to more complex models of intracortical connectivity. It may also be applied to other developmental phenomena including the development of lamination in the LGN (Shatz and Stryker, 1988; Hahm et al., 1991), the formation of visual maps in experimentally altered auditory cortex (Roe et al., 1990, 1992), and the mapping of visual and auditory maps in the optic tectum (Knudsen and Brainard, 1991; Brainard and Knudsen, 1993). For each system the goal is to develop testable predictions as to the patterns of activity and connectivity that could or could not lead to the results observed given a proposed mechanism of plasticity. Incorporation of deeper levels of biophysical realism will extend, deepen, and perhaps fundamentally alter the framework presented here. An important goal for the future will be to understand the computational and functional significance of developmental rules. Activity-dependent, competitive mechanisms of synaptic plasticity appear to play an important role in many processes of late neural development, where an initially rough connectivity pattern refines to a precise, mature pattern. A prominent example is the formation of ocular dominance columns in the visual cortex of many mammals. These processes may be modeled at several levels.(ABSTRACT TRUNCATED AT 400 WORDS)

• “Push-Pull” inhibition counteracts untuned DC from LGN • Achieves contrast invariant orientation tuning for cortical excitatory cells • Inhibitory cells follow tuning of LGN: sum of tuned F1 term and untuned DC term • Correlation-based connectivity derived from developmentally motivated learning rules for intracortical synaptic connections • Excitatory connections between cells with correlated receptive fields: same orientation and same spatial phase • Inhibitory connections between cells with anticorrelated receptive fields: same orienatation and opposite spatial phase • Synaptic depression model follows the model of Abbott et al, 1997. • First spike in a spike train elicits strongest postsynaptic potential • Synaptic strength weakens with each spike in rate dependent manner • Synapses return to full strength with time constant ~ 100 msec

Development of orientation-selective receptive fields in primary visual cortex of higher mammals can occur through activity-dependent competition between ON-center and OFF-center inputs. This competition yields orientation and spatial-frequency-selective `simple cells' if the dark activity of ON (or OFF)-center inputs is best correlated with that of other ON (or OFF)-center inputs at small retinotopic separations and with that of OFF (ON)-center inputs at larger separations. Features of cat and monkey cortical organization emerge, including continuous and periodic arrangement of preferred orientation across the cortex. A new feature, systematic variation of receptive field spatial phase, is predicted. Experimental tests of this hypothesis are proposed.

The visual cortex of many adult mammals has patches of cells that receive inputs driven by the right eye alternating with patches that receive inputs driven by the left eye. These ocular dominance patches (or "columns") form during early life as a consequence of competition between the activity patterns of the two eyes. A mathematical model of several biological mechanisms that can account for this development is presented. Analysis of this model reveals the conditions under which ocular dominance segregation will occur and determines the resulting patch width. Simulations of the model also exhibit other phenomena associated with early visual development, such as topographic refinement of cortical receptive fields, the confinement of input cell connections to patches, monocular deprivation plasticity including a critical period, and the effect of artificially induced strabismus. The model can be used to predict the results of proposed experiments and to discriminate among various mechanisms of plasticity.

We have investigated the role of the N-methyl-D-aspartate (NMDA) receptor, a subtype of glutamate receptor, in the responses of cells in adult cat visual cortex. After intracortical infusion of the NMDA receptor antagonist DL-2-amino-5-phosphonovalerate (DL-APV) for one day, iontophoretic responses to NMDA, to kainate, and to quisqualate revealed a receptor blockade specific to NMDA receptors and extending several millimeters from the cannula. In this region, neuronal responses to visual stimulation were profoundly suppressed, in a manner strongly correlated with the degree of NMDA receptor blockade. Neither NMDA receptor blockade nor activity suppression was caused by the inactive stereoisomer L-APV. Hence, we conclude that NMDA receptors make a major contribution to normal excitatory transmission in adult visual cortex.