Inhibition, spike threshold, and stimulus selectivity in primary visual cortex

Emergence of spatially periodic diffusive waves in small-world neuronal networks

It has been observed in experiment that the anatomical structure of neuronal networks in the brain possesses the feature of small-world networks. Yet how the small-world structure affects network dynamics remains to be fully clarified. Here we study the dynamics of a class of small-world networks consisting of pulse-coupled integrate-and-fire (I&F) neurons. Under stochastic Poisson drive, we find that the activity of the entire network resembles diffusive waves. To understand its underlying mechanism, we analyze the simplified regular-lattice network consisting of firing-rate-based neurons as an approximation to the original I&F small-world network. We demonstrate both analytically and numerically that, with strongly coupled connections, in the absence of noise, the activity of the firing-rate-based regular-lattice network spatially forms a static grating pattern that corresponds to the spatial distribution of the firing rate observed in the I&F small-world neuronal network. We further show that the spatial grating pattern with different phases comprise the continuous attractor of both the I&F small-world and firing-rate-based regular-lattice network dynamics. In the presence of input noise, the activity of both networks is perturbed along the continuous attractor, which gives rise to the diffusive waves. Our numerical simulations and theoretical analysis may potentially provide insights into the understanding of the generation of wave patterns observed in cortical networks.

A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron types to sensory processing and network synchronization in primate visual cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell type (››30% of neurons) that has narrow-waveform (NW) action potentials and high spontaneous discharge rates and fires in high-frequency bursts. These neurons are more stimulus selective and phase locked to 30- to 80-Hz gamma oscillations than other neuron types. Unlike other neuron types, their gamma-phase locking is highly predictive of orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. We did not find a similar class of NW bursting neurons in L2-L4 of mouse V1. Given its properties, this class of NW bursting neurons should be pivotal for the encoding and transmission of stimulus information.

Representational untangling by the firing rate nonlinearity in V1 simple cells

An important computational goal of the visual system is ‘representational untangling’ (RU): representing increasingly complex features of visual scenes in an easily decodable format. RU is typically assumed to be achieved in high-level visual cortices via several stages of cortical processing. Here we show, using a canonical population coding model, that RU of low-level orientation information is already performed at the first cortical stage of visual processing, but not before that, by a fundamental cellular-level property: the thresholded firing rate nonlinearity of simple cells in the primary visual cortex (V1). We identified specific, experimentally measurable parameters that determined the optimal firing threshold for RU and found that the thresholds of V1 simple cells extracted from in vivo recordings in awake behaving mice were near optimal. These results suggest that information re-formatting, rather than maximisation, may already be a relevant computational goal for the early visual system.

The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability

Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception.

•

A simple network model explains stimulus-tuning of cortical variability suppression

•

Inhibition stabilizes recurrently interacting neurons with supralinear I/O functions

•

Stimuli strengthen inhibitory stabilization around a stable state, quenching variability

•

Single-trial V1 data are compatible with this model and rules out competing proposals

A case study in the functional consequences of scaling the sizes of realistic cortical models

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

With the vast numbers of neurons in the cerebral cortex, models in neuroscience are, for practical reasons, often orders of magnitude smaller than the actual network. We examine in this article the scalability of cortical networks. We find that function and dynamics in a network depend on network size. For illustration, we use a previously constructed realistic model of monkey visual cortex. Reducing the number of cells in the model, we find that small changes in synaptic weights can help maintain firing rates. However, model characteristics change fundamentally in the reduced models. Neurons have abnormal intracellular dynamics. The model becomes dominated by feedforward inputs and is less capable of functions requiring neuronal interaction. Modelers need to be aware of the potential issues with reduced cortical network models.

Differential Inhibitory Configurations Segregate Frequency Selectivity in the Mouse Inferior Colliculus

Receptive fields and tuning curves of sensory neurons represent the neural substrates that allow animals to efficiently detect and distinguish external stimuli. They are progressively refined to create diverse sensitivity and selectivity for neurons along ascending central pathways. However, the neural circuitry mechanisms have not been directly determined for such fundamental qualities in relation to sensory neurons' functional organizations, because of the technical difficulty of correlating neurons' input and output. Here, we obtained spike outputs and synaptic inputs from the same neurons within characteristically defined neural ensembles, to determine the synaptic mechanisms driving their diverse frequency selectivity in the mouse inferior colliculus. We find that the synaptic strength and timing of excitatory and inhibitory inputs are configured differently and independently within individual neurons' receptive fields, which segregate sensitive and selective neurons and endow neural populations with broad receptive fields and sharp frequency tuning. By computationally modeling spike outputs from integrating synaptic inputs and comparing them with real spike responses of the same neurons, we show that space-clamping errors did not qualitatively affect the estimation of spike responses derived from synaptic currents in in vivo voltage-clamp recordings. These data suggest that heterogeneous inhibitory circuits coexist locally for a parallel but differentiated representation of incoming signals.SIGNIFICANCE STATEMENT Sensitivity and selectivity are functional qualities of sensory systems to facilitate animals' survival. There is little direct evidence for the synaptic basis of neurons' functional variance within neural ensembles. Here we adopted a novel framework to fill such a long-standing gap by uniting population activities with single cells' spike outputs and their synaptic inputs. Furthermore, the effects of space-clamping errors on subcortical synaptic currents were evaluated in vivo, by comparing recorded spike responses and simulated spike outputs from computationally integrating synaptic inputs. Our study illustrated that the synaptic strength and timing of inhibition relative to excitation can be configured differently for neurons within a defined neural ensemble, to segregate their selectivity. It provides new insights into coexisting heterogeneous local circuits.

Dynamical patterns underlying response properties of cortical circuits

Recent experimental studies show cortical circuit responses to external stimuli display varied dynamical properties. These include stimulus strength-dependent population response patterns, a shift from synchronous to asynchronous states and a decline in neural variability. To elucidate the mechanisms underlying these response properties and explore how they are mechanistically related, we develop a neural circuit model that incorporates two essential features widely observed in the cerebral cortex. The first feature is a balance between excitatory and inhibitory inputs to individual neurons; the second feature is distance-dependent connectivity. We show that applying a weak external stimulus to the model evokes a wave pattern propagating along lateral connections, but a strong external stimulus triggers a localized pattern; these stimulus strength-dependent population response patterns are quantitatively comparable with those measured in experimental studies. We identify network mechanisms underlying this population response, and demonstrate that the dynamics of population-level response patterns can explain a range of prominent features in neural responses, including changes to the dynamics of neurons' membrane potentials and synaptic inputs that characterize the shift of cortical states, and the stimulus-evoked decline in neuron response variability. Our study provides a unified population activity pattern-based view of diverse cortical response properties, thus shedding new insights into cortical processing.

Randomly weighted receptor inputs can explain the large diversity of colour-coding neurons in the bee visual system

True colour vision requires comparing the responses of different spectral classes of photoreceptors. In insects, there is a wealth of data available on the physiology of photoreceptors and on colour-dependent behaviour, but less is known about the neural mechanisms that link the two. The available information in bees indicates a diversity of colour opponent neurons in the visual optic ganglia that significantly exceeds that known in humans and other primates. Here, we present a simple mathematical model for colour processing in the optic lobes of bees to explore how this diversity might arise. We found that the model can reproduce the physiological spectral tuning curves of the 22 neurons that have been described so far. Moreover, the distribution of the presynaptic weights in the model suggests that colour-coding neurons are likely to be wired up to the receptor inputs randomly. The perceptual distances in our random synaptic weight model are in agreement with behavioural observations. Our results support the idea that the insect nervous system might adopt partially random wiring of neurons for colour processing.

Mechanisms of Spatiotemporal Selectivity in Cortical Area MT

Cortical sensory neurons are characterized by selectivity to stimulation. This selectivity was originally viewed as a part of the fundamental "receptive field" characteristic of neurons. This view was later challenged by evidence that receptive fields are modulated by stimuli outside of the classical receptive field. Here, we show that even this modified view of selectivity needs revision. We measured spatial frequency selectivity of neurons in cortical area MT of alert monkeys and found that their selectivity strongly depends on luminance contrast, shifting to higher spatial frequencies as contrast increases. The changes of preferred spatial frequency are large at low temporal frequency, and they decrease monotonically as temporal frequency increases. That is, even interactions among basic stimulus dimensions of luminance contrast, spatial frequency, and temporal frequency strongly influence neuronal selectivity. This dynamic nature of neuronal selectivity is inconsistent with the notion of stimulus preference as a stable characteristic of cortical neurons.

Different Inhibitory Interneuron Cell Classes Make Distinct Contributions to Visual Contrast Perception

While recent work has revealed how different inhibitory interneurons influence responses of cortical neurons to sensory stimuli, little is known about their distinct contributions to sensory perception. Here, we optogenetically activated different genetically defined interneurons [parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP)] in visual cortex (V1) of mice working at threshold in a contrast increment detection task. The visual stimulus was paired with optogenetic stimulation to assess how enhancing V1 inhibitory neuron activity during visual processing altered task performance. PV or SST activation impaired, while VIP stimulation improved, contrast increment detection. The impairment produced by PV or SST activation persisted over several weeks of testing. In contrast, mice learned to reliably detect VIP activation in the absence of any natural visual stimulus. Thus, different inhibitory signals make distinct contributions to visual contrast perception.

Fine Control of Sound Frequency Tuning and Frequency Discrimination Acuity by Synaptic Zinc Signaling in Mouse Auditory Cortex

Neurons in the auditory cortex are tuned to specific ranges of sound frequencies. Although the cellular and network mechanisms underlying neuronal sound frequency selectivity are well studied and reflect the interplay of thalamocortical and intracortical excitatory inputs and further refinement by cortical inhibition, the precise synaptic signaling mechanisms remain less understood. To gain further understanding on these mechanisms and their effects on sound-driven behavior, we used in vivo imaging as well as behavioral approaches in awake and behaving female and male mice. We discovered that synaptic zinc, a modulator of neurotransmission and responsiveness to sound, sharpened the sound frequency tuning of principal and parvalbumin-expressing neurons and widened the sound frequency tuning of somatostatin-expressing inhibitory neurons in layer 2/3 of the primary auditory cortex. In the absence of cortical synaptic zinc, mice exhibited reduced acuity for detecting changes in sound frequencies. Together, our results reveal that cell-type-specific effects of zinc contribute to cortical sound frequency tuning and enhance acuity for sound frequency discrimination.SIGNIFICANCE STATEMENT Neuronal tuning to specific features of sensory stimuli is a fundamental property of cortical sensory processing that advantageously supports behavior. Despite the established roles of synaptic thalamocortical and intracortical excitation and inhibition in cortical tuning, the precise synaptic signaling mechanisms remain unknown. Here, we investigated these mechanisms in the mouse auditory cortex. We discovered a previously unknown signaling mechanism linking synaptic zinc signaling with cell-specific cortical tuning and enhancement in sound frequency discrimination acuity. Given the abundance of synaptic zinc in all sensory cortices, this newly discovered interaction between synaptic zinc and cortical tuning can provide a general mechanism for modulating neuronal stimulus specificity and sensory-driven behavior.

Nonlinear Lateral Interactions in V1 Population Responses Explained by a Contrast Gain Control Model

How do cortical responses to local image elements combine to form a spatial pattern of population activity in primate V1? Here, we used voltage-sensitive dye imaging, which measures summed membrane potential activity, to examine the rules that govern lateral interactions between the representations of two small local-oriented elements in macaque (Macaca mulatta) V1. We find strong subadditive and mostly orientation-independent interactions for nearby elements [2-4 mm interelement cortical distance (IED)] that gradually become linear at larger separations (>6 mm IED). These results are consistent with a population gain control model describing nonlinear V1 population responses to single oriented elements. However, because of the membrane potential-to-spiking accelerating nonlinearity, the model predicts supra-additive lateral interactions of spiking responses for intermediate separations at a range of locations between the two elements, consistent with some prior facilitatory effects observed in electrophysiology and psychophysics. Overall, our results suggest that population-level lateral interactions in V1 are primarily explained by a simple orientation-independent contrast gain control mechanism.SIGNIFICANCE STATEMENT Interactions between representations of simple visual elements such as oriented edges in primary visual cortex (V1) are thought to contribute to our ability to easily integrate contours and segment surfaces, but the mechanisms that govern these interactions are primarily unknown. Our study provides novel evidence that lateral interactions at the population level are governed by a simple contrast gain-control mechanism, and we show how this divisive gain-control mechanism can give rise to apparently facilitatory spiking responses.

Competing inhibition-stabilized networks in sensory and memory processing

In simplified models of neocortical circuits, inhibition is either modeled in a feedforward manner or through mutual inhibitory interactions that provide for competition between neuronal populations. By contrast, recent work has suggested a critical role for recurrent inhibition as a negative feedback element that stabilizes otherwise unstable recurrent excitation. Here, we show how models based upon a motif of recurrently connected "E-I" pairs of excitatory and inhibitory units can be used to describe experimental observations in sensory and memory networks. In a sensory network model of binocular rivalry, a model based on competing E-I motifs captures psychophysical observations about how incongruous images presented to the two eyes compete. In a model of cortical working memory, an architecturally similar model with modified synaptic time constants can mathematically accumulate signals into a working memory buffer in a manner that is robust to the abrupt removal of cells. These results suggest the inhibition-stabilized E-I motif as a fundamental building block for models of a wide array of neocortical dynamics.

Linking V1 Activity to Behavior

A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while nonhuman primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach-the decoder linking model (DLM) framework-in which candidate decoding models take neural responses as input and generate predicted behavior as output. The ultimate goal in this framework is to find the actual decoder-the model that best predicts behavior from neural responses. We discuss key relevant properties of primate V1 and review current literature from the DLM perspective. We conclude by discussing major technological and theoretical advances that are likely to accelerate our understanding of the link between V1 activity and behavior.

Linear Summation Underlies Direction Selectivity in Drosophila

While linear mechanisms lay the foundations of feature selectivity in many brain areas, direction selectivity in the elementary motion detector (EMD) of the fly has become a paradigm of nonlinear neuronal computation. We have bridged this divide by demonstrating that linear spatial summation can generate direction selectivity in the fruit fly Drosophila. Using linear systems analysis and two-photon imaging of a genetically encoded voltage indicator, we measure the emergence of direction-selective (DS) voltage signals in the Drosophila OFF pathway. Our study is a direct, quantitative investigation of the algorithm underlying directional signals, with the striking finding that linear spatial summation is sufficient for the emergence of direction selectivity. A linear stage of the fly EMD strongly resembles similar computations in vertebrate visual cortex, demands a reappraisal of the role of upstream nonlinearities, and implicates the voltage-to-calcium transformation in the refinement of feature selectivity in this system. VIDEO ABSTRACT.

Differential tuning of excitation and inhibition shapes direction selectivity in ferret visual cortex

To encode specific sensory inputs, cortical neurons must generate selective responses for distinct stimulus features. In principle, a variety of factors contribute to a cortical neuron’s response selectivity: the tuning and strength of excitatory^1–3 and inhibitory synaptic inputs^4–6, dendritic nonlinearities^7–9, and spike threshold^10,11. Here we employ a combination of techniques including in vivo whole-cell recording, synaptic and cellular resolution in vivo two photon calcium imaging, and GABAergic-selective optogenetic manipulation to dissect the factors contributing to direction selective responses of layer 2/3 neurons in ferret visual cortex (V1). Two-photon calcium imaging of dendritic spines^12,13 revealed that each neuron receives a mixture of excitatory synaptic inputs selective for the somatic preferred or null direction of motion. The relative number of preferred- and null-tuned excitatory inputs predicted a neuron’s somatic direction preference, but failed to account for the degree of direction selectivity. In contrast, in vivo whole-cell patch clamp recordings revealed a striking degree of direction selectivity in subthreshold responses that was significantly correlated with spiking direction selectivity. Subthreshold direction selectivity was predicted by the magnitude and variance of the response to the null direction of motion, and several lines of evidence including conductance measurements demonstrate that differential tuning of excitation and inhibition suppresses responses to the null direction of motion. Consistent with this idea, optogenetic inactivation of GABAergic neurons in layer 2/3 reduced direction selectivity by enhancing responses to the null direction. Furthermore, using a new technique to optogenetically map connections of inhibitory neurons in layer 2/3 in vivo, we find that layer 2/3 inhibitory neurons make long-range, intercolumnar projections to excitatory neurons that prefer the opposite direction of motion. We conclude that intracortical inhibition exerts a major influence on the degree of direction selectivity in layer 2/3 of ferret V1 by suppressing responses to the null direction of motion.

Transformation of Feature Selectivity From Membrane Potential to Spikes in the Mouse Superior Colliculus

Neurons in the visual system display varying degrees of selectivity for stimulus features such as orientation and direction. Such feature selectivity is generated and processed by intricate circuit and synaptic mechanisms. A key factor in this process is the input-output transformation from membrane potential (V_m) to spikes in individual neurons. Here, we use in vivo whole-cell recording to study V_m-to-spike transformation of visual feature selectivity in the superficial neurons of the mouse superior colliculus (SC). As expected from the spike threshold effect, direction and orientation selectivity increase from V_m to spike responses. The degree of this increase is highly variable, and interestingly, it is correlated with the receptive field size of the recorded neurons. We find that the relationships between V_m and spike rate and between V_m dynamics and spike initiation are also correlated with receptive field size, which likely contribute to the observed input-output transformation of feature selectivity. Together, our findings provide useful information for understanding information processing and visual transformation in the mouse SC.

E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks

Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks

Brain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found that V1 neurons’ average responses were primarily additive (linear). We used a recurrent cortical network model to determine whether these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. Simulations showed that cortical input-output transformations can be changed from linear to sublinear with moderate (∼20%) strengthening of connections between inhibitory neurons, but this change away from linear scaling depends on the presence of feedforward inhibition. Simulating a variety of recurrent connection strengths showed that, compared with when input arrives only to excitatory neurons, networks produce a wider range of output spiking responses in the presence of feedforward inhibition.

Stabilized supralinear network can give rise to bistable, oscillatory, and persistent activity

Many fundamental neural computations from normalization to rhythm generation emerge from the same cortical hardware, but they often require dedicated models to explain each phenomenon. Recently, the stabilized supralinear network (SSN) model has been used to explain a variety of nonlinear integration phenomena such as normalization, surround suppression, and contrast invariance. However, cortical circuits are also capable of implementing working memory and oscillations which are often associated with distinct model classes. Here, we show that the SSN motif can serve as a universal circuit model that is sufficient to support not only stimulus integration phenomena but also persistent states, self-sustained network-wide oscillations along with two coexisting stable states that have been linked with working memory.

A hallmark of cortical circuits is their versatility. They can perform multiple fundamental computations such as normalization, memory storage, and rhythm generation. Yet it is far from clear how such versatility can be achieved in a single circuit, given that specialized models are often needed to replicate each computation. Here, we show that the stabilized supralinear network (SSN) model, which was originally proposed for sensory integration phenomena such as contrast invariance, normalization, and surround suppression, can give rise to dynamic cortical features of working memory, persistent activity, and rhythm generation. We study the SSN model analytically and uncover regimes where it can provide a substrate for working memory by supporting two stable steady states. Furthermore, we prove that the SSN model can sustain finite firing rates following input withdrawal and present an exact connectivity condition for such persistent activity. In addition, we show that the SSN model can undergo a supercritical Hopf bifurcation and generate global oscillations. Based on the SSN model, we outline the synaptic and neuronal mechanisms underlying computational versatility of cortical circuits. Our work shows that the SSN is an exactly solvable nonlinear recurrent neural network model that could pave the way for a unified theory of cortical function.

Differences in the Electrophysiological Properties of Mouse Somatosensory Layer 2/3 Neurons In Vivo and Slice Stem from Intrinsic Sources Rather than a Network-Generated High Conductance State

Synaptic activity in vivo can potentially alter the integration properties of neurons. Using recordings in awake mice, we targeted somatosensory layer 2/3 pyramidal neurons and compared neuronal properties with those from slices. Pyramidal cells in vivo had lower resistance and gain values, as well as broader spikes and increased spike frequency adaptation compared to the same cells in slices. Increasing conductance in neurons using dynamic clamp to levels observed in vivo, however, did not lessen the differences between in vivo and slice conditions. Further, local application of tetrodotoxin (TTX) in vivo blocked synaptic-mediated membrane voltage fluctuations but had little impact on pyramidal cell membrane input resistance and time constant values. Differences in electrophysiological properties of layer 2/3 neurons in mouse somatosensory cortex, therefore, stem from intrinsic sources separate from synaptic-mediated membrane voltage fluctuations.

Adaptation to stimulus orientation in mouse primary visual cortex

Information processing in the visual system is shaped by recent stimulus history, such that prolonged viewing of an adapting stimulus can alter the perception of subsequently presented test stimuli. In the tilt-after-effect, the perceived orientation of a grating is often repelled away from the orientation of a previously viewed adapting grating. A possible neural correlate for the tilt-after-effect has been described in cat and macaque primary visual cortex (V1), where adaptation produces repulsive shifts in the orientation tuning curves of V1 neurons. We investigated adaptation to stimulus orientation in mouse V1 to determine whether known species differences in orientation processing, notably V1 functional architecture and proportion of tightly tuned cells, are important for these repulsive shifts. Unlike the consistent repulsion reported in other species, we found that repulsion was only about twice as common as attraction in our mouse data. Furthermore, adapted responses were attenuated across all orientations. A simple model that captured key physiological findings reported in cats and mice indicated that the greater proportion of broadly tuned neurons in mice may explain the observed species differences in adaptation.

Neuronal Population Activity in Spinal Motor Circuits: Greater Than the Sum of Its Parts

The core elements of stereotypical movements such as locomotion, scratching and breathing are generated by networks in the lower brainstem and the spinal cord. Ensemble activities in spinal motor networks had until recently been merely a black box, but with the emergence of ultra-thin Silicon multi-electrode technology it was possible to reveal the spiking activity of larger parts of the network. A series of experiments revealed unexpected features of spinal networks, such as multiple spiking regimes and lognormal firing rate distributions. The lognormality renders the widespread idea of a typical firing rate ± standard deviation an ill-suited description, and therefore these findings define a new arithmetic of motor networks. Focusing on the population activity behind motor pattern generation this review summarizes this advance and discusses its implications.

A Unifying Motif for Spatial and Directional Surround Suppression

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. Although 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 pharmacological 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 STATEMENT Visual 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.

Orientation Tuning of Correlated Activity in the Developing Lateral Geniculate Nucleus

Neural circuits and the cells that comprise them undergo developmental changes in the spatial organization of their connections and in their temporal response properties. Within the lateral geniculate nucleus (LGN) of the dorsal thalamus, these changes have pronounced effects on the spatiotemporal receptive fields (STRFs) of neurons. An open and unresolved question is how STRF maturation affects stimulus-evoked correlated activity between pairs of LGN neurons during development. This is an important question to answer because stimulus-evoked correlated activity likely plays a role in establishing the specificity of thalamocortical connectivity and the receptive fields (RFs) of postsynaptic cortical neurons. Using multielectrode recording methods and white noise stimuli, we recorded neural activity from ensembles of LGN neurons in cats across early development. As expected, there was a progressive maturation of the spatial and temporal properties of visual responses. Using drifting bar stimuli and cross-correlation analysis, we also determined the orientation-tuning bandwidth of correlated activity between pairs of LGN neurons at different stages of development (Sillito and Jones, 2002; Andolina et al., 2007; Stanley et al., 2012; Kelly et al., 2014). Despite the larger RFs and slower responses of immature LGN neurons compared with mature neurons, our results show that correlated activity in the LGN was as tightly tuned for orientation early in development as it was in the adult. Closer examination revealed this age-invariant orientation tuning of correlated activity likely involves cellular mechanisms related to spike fatigue in young animals and a progressive decrease in response latency with development.SIGNIFICANCE STATEMENT Orientation tuning is a fundamental property of neurons in primary visual cortex. An important and unresolved question is how orientation tuning emerges during brain development. This study explores a potential mechanism for the establishment of orientation tuning based on correlated activity patterns among ensembles of maturing neurons in the lateral geniculate nucleus (LGN) of the thalamus. Results show that correlated activity between pairs of LGN neurons is more tightly tuned than predictions based simply on receptive field size, indicating that correlated activity has the properties needed to play an important role in the development of geniculocortical circuits and the emergence of cortical orientation tuning.

Adaptation without Plasticity

Sensory adaptation is a phenomenon in which neurons are affected not only by their immediate input but also by the sequence of preceding inputs. In visual cortex, for example, neurons shift their preferred orientation after exposure to an oriented stimulus. This adaptation is traditionally attributed to plasticity. We show that a recurrent network generates tuning curve shifts observed in cat and macaque visual cortex, even when all synaptic weights and intrinsic properties in the model are fixed. This demonstrates that, in a recurrent network, adaptation on timescales of hundreds of milliseconds does not require plasticity. Given the ubiquity of recurrent connections, this phenomenon likely contributes to responses observed across cortex and shows that plasticity cannot be inferred solely from changes in tuning on these timescales. More broadly, our findings show that recurrent connections can endow a network with a powerful mechanism to store and integrate recent contextual information.

Local Order within Global Disorder: Synaptic Architecture of Visual Space

Substantial evidence at the subcellular level indicates that the spatial arrangement of synaptic inputs onto dendrites could play a significant role in cortical computations, but how synapses of functionally defined cortical networks are arranged within the dendrites of individual neurons remains unclear. Here we assessed one-dimensional spatial receptive fields of individual dendritic spines within individual layer 2/3 neuron dendrites. Spatial receptive field properties of dendritic spines were strikingly diverse, with no evidence of large-scale topographic organization. At a fine scale, organization was evident: neighboring spines separated by less than 10 μm shared similar spatial receptive field properties and exhibited a distance-dependent correlation in sensory-driven and spontaneous activity patterns. Fine-scale dendritic organization was supported by the fact that functional groups of spines defined by dimensionality reduction of receptive field properties exhibited non-random dendritic clustering. Our results demonstrate that functional synaptic clustering is a robust feature existing at a local spatial scale. VIDEO ABSTRACT.

Serotonin Decreases the Gain of Visual Responses in Awake Macaque V1

Serotonin, an important neuromodulator in the brain, is implicated in affective and cognitive functions. However, its role even for basic cortical processes is controversial. For example, in the mammalian primary visual cortex (V1), heterogenous serotonergic modulation has been observed in anesthetized animals. Here, we combined extracellular single-unit recordings with iontophoresis in awake animals. We examined the role of serotonin on well-defined tuning properties (orientation, spatial frequency, contrast, and size) in V1 of two male macaque monkeys. We find that in the awake macaque the modulatory effect of serotonin is surprisingly uniform: it causes a mainly multiplicative decrease of the visual responses and a slight increase in the stimulus-selective response latency. Moreover, serotonin neither systematically changes the selectivity or variability of the response, nor the interneuronal correlation unexplained by the stimulus ("noise-correlation"). The modulation by serotonin has qualitative similarities with that for a decrease in stimulus contrast, but differs quantitatively from decreasing contrast. It can be captured by a simple additive change to a threshold-linear spiking nonlinearity. Together, our results show that serotonin is well suited to control the response gain of neurons in V1 depending on the animal's behavioral or motivational context, complementing other known state-dependent gain-control mechanisms.SIGNIFICANCE STATEMENT Serotonin is an important neuromodulator in the brain and a major target for drugs used to treat psychiatric disorders. Nonetheless, surprisingly little is known about how it shapes information processing in sensory areas. Here we examined the serotonergic modulation of visual processing in the primary visual cortex of awake behaving macaque monkeys. We found that serotonin mainly decreased the gain of the visual responses, without systematically changing their selectivity, variability, or covariability. This identifies a simple computational function of serotonin for state-dependent sensory processing, depending on the animal's affective or motivational state.

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

Human Occipital and Parietal GABA Selectively Influence Visual Perception of Orientation and Size

GABA is the primary inhibitory neurotransmitter in human brain. The level of GABA varies substantially across individuals, and this variability is associated with interindividual differences in visual perception. However, it remains unclear whether the association between GABA level and visual perception reflects a general influence of visual inhibition or whether the GABA levels of different cortical regions selectively influence perception of different visual features. To address this, we studied how the GABA levels of parietal and occipital cortices related to interindividual differences in size, orientation, and brightness perception. We used visual contextual illusion as a perceptual assay since the illusion dissociates perceptual content from stimulus content and the magnitude of the illusion reflects the effect of visual inhibition. Across individuals, we observed selective correlations between the level of GABA and the magnitude of contextual illusion. Specifically, parietal GABA level correlated with size illusion magnitude but not with orientation or brightness illusion magnitude; in contrast, occipital GABA level correlated with orientation illusion magnitude but not with size or brightness illusion magnitude. Our findings reveal a region- and feature-dependent influence of GABA level on human visual perception. Parietal and occipital cortices contain, respectively, topographic maps of size and orientation preference in which neural responses to stimulus sizes and stimulus orientations are modulated by intraregional lateral connections. We propose that these lateral connections may underlie the selective influence of GABA on visual perception.

SIGNIFICANCE STATEMENT GABA, the primary inhibitory neurotransmitter in human visual system, varies substantially across individuals. This interindividual variability in GABA level is linked to interindividual differences in many aspects of visual perception. However, the widespread influence of GABA raises the question of whether interindividual variability in GABA reflects an overall variability in visual inhibition and has a general influence on visual perception or whether the GABA levels of different cortical regions have selective influence on perception of different visual features. Here we report a region- and feature-dependent influence of GABA level on human visual perception. Our findings suggest that GABA level of a cortical region selectively influences perception of visual features that are topographically mapped in this region through intraregional lateral connections.

UP-DOWN cortical dynamics reflect state transitions in a bistable network

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model.

SIGNIFICANCE STATEMENT

We present the first realistic model that has captured the sparseness of magnocellular LGN inputs to the macaque primary visual cortex and successfully derived orientation selectivity from them. Three implications are (1) even in input layers to the visual cortex, the system is less feedforward and more dominated by intracortical signals than previously thought, (2) interactions among cortical neurons in local populations produce dynamics not explained by single neurons, and (3) such dynamics are important for function. Our model also shows that a comprehensive picture is necessary to explain function, because different visual properties are related. This study points to the need for paradigm shifts in neuroscience modeling: greater emphasis on population dynamics and, where possible, a move toward data-driven, comprehensive models.

Synaptic integration in cortical inhibitory neuron dendrites

Cortical inhibitory interneurons have a wide range of important functions, including balancing network excitation, enhancing spike-time precision of principal neurons, and synchronizing neural activity within and across brain regions. All these functions critically depend on the integration of synaptic inputs in their dendrites. But the sparse number of inhibitory cells, their small caliber dendrites, and the problem of cell-type identification, have prevented fast progress in analyzing their dendritic properties. Despite these challenges, recent advancements in electrophysiological, optical and molecular tools have opened the door for studying synaptic integration and dendritic computations in molecularly defined inhibitory interneurons. Accumulating evidence indicates that the biophysical properties of inhibitory neuron dendrites differ substantially from those of pyramidal neurons. In addition to the supralinear dendritic integration commonly observed in pyramidal neurons, interneuron dendrites can also integrate synaptic inputs in a linear or sublinear fashion. In this comprehensive review, we compare the dendritic biophysical properties of the three major classes of cortical inhibitory neurons and discuss how these cell type-specific properties may support their functions in the cortex.

Retinal and Nonretinal Contributions to Extraclassical Surround Suppression in the Lateral Geniculate Nucleus

Extraclassical surround suppression is a prominent receptive field property of neurons in the lateral geniculate nucleus (LGN) of the dorsal thalamus, influencing stimulus size tuning, response gain control, and temporal features of visual responses. Despite evidence for the involvement of both retinal and nonretinal circuits in the generation of extraclassical suppression, we lack an understanding of the relative roles played by these pathways and how they interact during visual stimulation. To determine the contribution of retinal and nonretinal mechanisms to extraclassical suppression in the feline, we made simultaneous single-unit recordings from synaptically connected retinal ganglion cells and LGN neurons and measured the influence of stimulus size on the spiking activity of presynaptic and postsynaptic neurons. Results show that extraclassical suppression is significantly stronger for LGN neurons than for their retinal inputs, indicating a role for extraretinal mechanisms. Further analysis revealed that the enhanced suppression can be accounted for by mechanisms that suppress the effectiveness of retinal inputs in evoking LGN spikes. Finally, an examination of the time course for the onset of extraclassical suppression in the LGN and the size-dependent modulation of retinal spike efficacy suggests the early phase of augmented suppression involves local thalamic circuits. Together, these results demonstrate that the LGN is much more than a simple relay for retinal signals to cortex; it also filters retinal spikes dynamically on the basis of stimulus statistics to adjust the gain of visual signals delivered to cortex.

SIGNIFICANCE STATEMENT

The lateral geniculate nucleus (LGN) is the gateway through which retinal information reaches the cerebral cortex. Within the LGN, neuronal responses are often suppressed by stimuli that extend beyond the classical receptive field. This form of suppression, called extraclassical suppression, serves to adjust the size tuning, response gain, and temporal response properties of neurons. Given the important influence of extraclassical suppression on visual signals delivered to cortex, we performed experiments to determine the circuit mechanisms that contribute to extraclassical suppression in the LGN. Results show that suppression is augmented beyond that provided by direct retinal inputs and delayed, consistent with polysynaptic inhibition. Importantly, these mechanisms influence the effectiveness of incoming retinal signals, thereby filtering the signals ultimately conveyed to cortex.

Visually Driven Neuropil Activity and Information Encoding in Mouse Primary Visual Cortex

Cortical neuropil modulations recorded by calcium imaging reflect the activity of large aggregates of axo-dendritic processes and synaptic compartments from a large number of neurons. The organization of this activity impacts neuronal firing but is not well understood. Here we used in vivo 2-photon imaging with Oregon Green Bapta (OGB) and GCaMP6s to study neuropil visual responses to moving gratings in layer 2/3 of mouse area V1. We found neuropil responses to be strongly modulated and more reliable than neighboring somatic activity. Furthermore, stimulus independent modulations in neuropil activity, i.e., noise correlations, were highly coherent across the cortical surface, up to distances of at least 200 μm. Pairwise neuropil-to-neuropil-patch noise correlation strength was much higher than cell-to-cell noise correlation strength and depended strongly on brain state, decreasing in quiet wakefulness relative to light anesthesia. The profile of neuropil noise correlation strength decreased gently with distance, dropping by ~11% at a distance of 200 μm. This was comparatively slower than the profile of cell-to-cell noise correlations, which dropped by ~23% at 200 μm. Interestingly, in spite of the “salt & pepper” organization of orientation and direction encoding across mouse V1 neurons, populations of neuropil patches, even of moderately large size (radius ~100 μm), showed high accuracy for discriminating perpendicularly moving gratings. This was commensurate to the accuracy of corresponding cell populations. The dynamic, stimulus dependent, nature of neuropil activity further underscores the need to carefully separate neuropil from cell soma activity in contemporary imaging studies.

Functional Organization of Flash-Induced V1 Offline Reactivation

The primary visual cortex exhibits a late, long response with a latency of >300 ms and an immediate early response that occurs ∼100 ms after a visual stimulus. The late response is thought to contribute to visual functions such as sensory perception, iconic memory, working memory, and forming connections between temporally separated stimuli. However, how the visual late response is generated and organized is not completely understood. In the mouse primary visual cortex in vivo, we isolated long-delayed responses by using a brief light-flash stimulus for which the stimulus late response occurred long after the stimulus offset and was not contaminated by the instantaneous response evoked by the stimulus. Using whole-cell patch-clamp recordings, we demonstrated that the late rebound response was shaped by a net-balanced increase in excitatory and inhibitory synaptic conductances, whereas transient imbalances were caused by intermittent inhibitory barrage. In contrast to the common assumption that the neocortical late response reflects a feedback signal from the downstream higher-order cortical areas, our pharmacological and optogenetic analyses demonstrated that the late responses likely have a thalamic origin. Therefore, the late component of a sensory-evoked cortical response should be interpreted with caution.

SIGNIFICANCE STATEMENT

The long-delayed responses of neocortical neurons are thought to arise from cortical feedback activity that is related to sensory perception and cognition. The mechanism of neocortical late responses was investigated using multiple electrophysiological techniques and the findings indicate that it actually arises from the thalamus. In addition, during the late response, excitation and inhibition are balanced, but inhibition is dominant in patterning action potentials.

Network-Level Control of Frequency Tuning in Auditory Cortex

Lateral inhibition is a fundamental circuit operation that sharpens the tuning properties of cortical neurons. This operation is classically attributed to an increase in GABAergic synaptic input triggered by non-preferred stimuli. Here we use in vivo whole-cell recording and two-photon Ca²⁺ imaging in awake mice to show that lateral inhibition shapes frequency tuning in primary auditory cortex via an unconventional mechanism: non-preferred tones suppress both excitatory and inhibitory synaptic inputs onto layer 2/3 cells ("network suppression"). Moreover, optogenetic inactivation of inhibitory interneurons elicits a paradoxical increase in inhibitory synaptic input. These results indicate that GABAergic interneurons regulate cortical activity indirectly via the suppression of recurrent excitation. Furthermore, the network suppression underlying lateral inhibition was blocked by inactivation of somatostatin-expressing interneurons (SOM cells), but not parvalbumin-expressing interneurons (PV cells). Together, these findings reveal that SOM cells govern lateral inhibition and control cortical frequency tuning through the regulation of reverberating recurrent circuits.

Cortical amplification models of experience-dependent development of selective columns and response sparsification

The development of direction-selective cortical columns requires visual experience, but the neural circuits and plasticity mechanisms that are responsible for this developmental transition are unknown. To gain insight into the mechanisms that could underlie experience-dependent increases in selectivity, we explored families of cortical amplifier models that enhance weakly biased feedforward signals. Here we focused exclusively on possible contributions of cortico-cortical connections and took feedforward input to be constant. We modeled pairs of interconnected columns that received equal and oppositely biased inputs. In a single-element model of cortical columns, we found two ways that cortical columns could receive biased feedforward input and exhibit strong but unselective responses to stimuli: 1) within-column recurrent excitatory connections could be strong enough to amplify both strong and weak feedforward input, or 2) columns that received differently biased inputs could have strong excitatory cross-connections that destroy selectivity. A Hebbian plasticity rule combined with simulated experience with stimuli weakened these strong cross-connections across cortical columns, allowing the individual columns to respond selectively to their biased inputs. In a model that included both excitatory and inhibitory neurons in each column, an additional means of obtaining selectivity through the cortical circuit was uncovered: cross-column suppression of inhibition-stabilized networks. When each column operated as an inhibition-stabilized network, cross-column excitation onto inhibitory neurons forced competition between the columns but in a manner that did not involve strong null-direction inhibition, consistent with experimental measurements of direction selectivity in visual cortex. Experimental predictions of these possible contributions of cortical circuits are discussed.NEW & NOTEWORTHY Sensory circuits are initially constructed via mechanisms that are independent of sensory experience, but later refinement requires experience. We constructed models of how circuits that receive biased feedforward inputs can be initially unselective and then be modified by experience and plasticity so that the resulting circuit exhibits increased selectivity. We propose that neighboring cortical columns may initially exhibit coupling that is too strong for selectivity. Experience-dependent mechanisms decrease this coupling so individual columns can exhibit selectivity.

Neurophysiology and Regulation of the Balance Between Excitation and Inhibition in Neocortical Circuits

Brain function relies on the ability of neural networks to maintain stable levels of activity, while experiences sculpt them. In the neocortex, the balance between activity and stability relies on the coregulation of excitatory and inhibitory inputs onto principal neurons. Shifts of excitation or inhibition result in altered excitability impaired processing of incoming information. In many neurodevelopmental and neuropsychiatric disorders, the excitability of local circuits is altered, suggesting that their pathophysiology may involve shifts in synaptic excitation, inhibition, or both. Most studies focused on identifying the cellular and molecular mechanisms controlling network excitability to assess whether they may be altered in animal models of disease. The impact of changes in excitation/inhibition balance on local circuit and network computations is not clear. Here we report findings on the integration of excitatory and inhibitory inputs in healthy cortical circuits and discuss how shifts in excitation/inhibition balance may relate to pathological phenotypes.

Circuits and Mechanisms for Surround Modulation in Visual Cortex

Surround modulation (SM) is a fundamental property of sensory neurons in many species and sensory modalities. SM is the ability of stimuli in the surround of a neuron's receptive field (RF) to modulate (typically suppress) the neuron's response to stimuli simultaneously presented inside the RF, a property thought to underlie optimal coding of sensory information and important perceptual functions. Understanding the circuit and mechanisms for SM can reveal fundamental principles of computations in sensory cortices, from mouse to human. Current debate is centered over whether feedforward or intracortical circuits generate SM, and whether this results from increased inhibition or reduced excitation. Here we present a working hypothesis, based on theoretical and experimental evidence, that SM results from feedforward, horizontal, and feedback interactions with local recurrent connections, via synaptic mechanisms involving both increased inhibition and reduced recurrent excitation. In particular, strong and balanced recurrent excitatory and inhibitory circuits play a crucial role in the computation of SM.

Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Responses of individual task-relevant sensory neurons can predict monkeys' trial-by-trial choices in perceptual decision-making tasks. Choice-correlated activity has been interpreted as evidence that the responses of these neurons are causally linked to perceptual judgments. To further test this hypothesis, we studied responses of orientation-selective neurons in V1 and V2 while two macaque monkeys performed a fine orientation discrimination task. Although both animals exhibited a high level of neuronal and behavioral sensitivity, only one exhibited choice-correlated activity. Surprisingly, this correlation was negative: when a neuron fired more vigorously, the animal was less likely to choose the orientation preferred by that neuron. Moreover, choice-correlated activity emerged late in the trial, earlier in V2 than in V1, and was correlated with anticipatory signals. Together, these results suggest that choice-correlated activity in task-relevant sensory neurons can reflect postdecision modulatory signals.SIGNIFICANCE STATEMENT When observers perform a difficult sensory discrimination, repeated presentations of the same stimulus can elicit different perceptual judgments. This behavioral variability often correlates with variability in the activity of sensory neurons driven by the stimulus. Traditionally, this correlation has been interpreted as suggesting a causal link between the activity of sensory neurons and perceptual judgments. More recently, it has been argued that the correlation instead may originate in recurrent input from other brain areas involved in the interpretation of sensory signals. Here, we call both hypotheses into question. We show that choice-related activity in sensory neurons can be highly variable across observers and can reflect modulatory processes that are dissociated from perceptual decision-making.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Visual Circuits for Direction Selectivity

Images projected onto the retina of an animal eye are rarely still. Instead, they usually contain motion signals originating either from moving objects or from retinal slip caused by self-motion. Accordingly, motion signals tell the animal in which direction a predator, prey, or the animal itself is moving. At the neural level, visual motion detection has been proposed to extract directional information by a delay-and-compare mechanism, representing a classic example of neural computation. Neurons responding selectively to motion in one but not in the other direction have been identified in many systems, most prominently in the mammalian retina and the fly optic lobe. Technological advances have now allowed researchers to characterize these neurons' upstream circuits in exquisite detail. Focusing on these upstream circuits, we review and compare recent progress in understanding the mechanisms that generate direction selectivity in the early visual system of mammals and flies.

Extending the Stabilized Supralinear Network model for binocular image processing

The visual cortex is both extensive and intricate. Computational models are needed to clarify the relationships between its local mechanisms and high-level functions. The Stabilized Supralinear Network (SSN) model was recently shown to account for many receptive field phenomena in V1, and also to predict subtle receptive field properties that were subsequently confirmed in vivo. In this study, we performed a preliminary exploration of whether the SSN is suitable for incorporation into large, functional models of the visual cortex, considering both its extensibility and computational tractability. First, whereas the SSN receives abstract orientation signals as input, we extended it to receive images (through a linear-nonlinear stage), and found that the extended version behaved similarly. Secondly, whereas the SSN had previously been studied in a monocular context, we found that it could also reproduce data on interocular transfer of surround suppression. Finally, we reformulated the SSN as a convolutional neural network, and found that it scaled well on parallel hardware. These results provide additional support for the plausibility of the SSN as a model of lateral interactions in V1, and suggest that the SSN is well suited as a component of complex vision models. Future work will use the SSN to explore relationships between local network interactions and sophisticated vision processes in large networks.

Lognormal firing rate distribution reveals prominent fluctuation-driven regime in spinal motor networks

When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a 'mean-driven' or a 'fluctuation-driven' regime. Fluctuation-driven neurons have a 'supralinear' input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the 'fluctuation-driven' regime regardless of behavior. Because of the disparity in input-output properties for these two regimes, this fraction may reflect a fine trade-off between stability and sensitivity in order to maintain flexibility across behaviors.

Mixed functional microarchitectures for orientation selectivity in the mouse primary visual cortex

A minicolumn is the smallest anatomical module in the cortical architecture, but it is still in debate whether it serves as functional units for cortical processing. In the rodent primary visual cortex (V1), neurons with different preferred orientations are mixed horizontally in a salt and pepper manner, but vertical functional organization was not examined. In this study, we found that neurons with similar orientation preference are weakly but significantly clustered vertically in a short length and horizontally in the scale of a minicolumn. Interestingly, the vertical clustering is found only in a part of minicolumns, and others are composed of neurons with a variety of orientation preferences. Thus, the mouse V1 is a mixture of vertical clusters of neurons with various degrees of orientation similarity, which may be the compromise between the brain size and keeping the vertical clusters of similarly tuned neurons at least in a subset of clusters.

Primary visual cortical neurons display mostly a salt and pepper arrangement of orientation preferences along the horizontal cortical axis. Here the authors show that a significant subset of minicolumns, one-cell wide arrays of cells arranged along the vertical axis, show similar orientation tuning preferences.