An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push-Pull and Broad Inhibition.

The spatial organization and dynamic interactions between excitatory and inhibitory synaptic inputs that define the receptive field (RF) of simple cells in the cat primary visual cortex (V1) still raise the following paradoxical issues: (1) stimulation of simple cells in V1 with drifting gratings supports a wiring schema of spatially segregated sets of excitatory and inhibitory inputs activated in an opponent way by stimulus contrast polarity and (2) in contrast, intracellular studies using flashed bars suggest that although ON and OFF excitatory inputs are indeed segregated, inhibitory inputs span the entire RF regardless of input contrast polarity. Here, we propose a biologically detailed computational model of simple cells embedded in a V1-like network that resolves this seeming contradiction. We varied parametrically the RF-correlation-based bias for excitatory and inhibitory synapses and found that a moderate bias of excitatory neurons to synapse onto other neurons with correlated receptive fields and a weaker bias of inhibitory neurons to synapse onto other neurons with anticorrelated receptive fields can explain the conductance input, the postsynaptic membrane potential, and the spike train dynamics under both stimulation paradigms. This computational study shows that the same structural model can reproduce the functional diversity of visual processing observed during different visual contexts.SIGNIFICANCE STATEMENT Identifying generic connectivity motives in cortical circuitry encoding for specific functions is crucial for understanding the computations implemented in the cortex. Indirect evidence points to correlation-based biases in the connectivity pattern in V1 of higher mammals, whereby excitatory and inhibitory neurons preferentially synapse onto neurons respectively with correlated and anticorrelated receptive fields. A recent intracellular study questions this push-pull hypothesis, failing to find spatial anticorrelation patterns between excitation and inhibition across the receptive field. We present here a spiking model of V1 that integrates relevant anatomic and physiological constraints and shows that a more versatile motif of correlation-based connectivity with selectively tuned excitation and broadened inhibition is sufficient to account for the diversity of functional descriptions obtained for different classes of stimuli.

Spontaneous cortical activity is transiently poised close to criticality.

Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

Synaptic Correlates of Low-Level Perception in V1.

The computational role of primary visual cortex (V1) in low-level perception remains largely debated. A dominant view assumes the prevalence of higher cortical areas and top-down processes in binding information across the visual field. Here, we investigated the role of long-distance intracortical connections in form and motion processing by measuring, with intracellular recordings, their synaptic impact on neurons in area 17 (V1) of the anesthetized cat. By systematically mapping synaptic responses to stimuli presented in the nonspiking surround of V1 receptive fields, we provide the first quantitative characterization of the lateral functional connectivity kernel of V1 neurons. Our results revealed at the population level two structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. First, subthreshold responses to oriented stimuli flashed in isolation in the nonspiking surround exhibited a geometric organization around the preferred orientation axis mirroring the psychophysical "association field" for collinear contour perception. Second, apparent motion stimuli, for which horizontal and feedforward synaptic inputs summed in-phase, evoked dominantly facilitatory nonlinear interactions, specifically during centripetal collinear activation along the preferred orientation axis, at saccadic-like speeds. This spatiotemporal integration property, which could constitute the neural correlate of a human perceptual bias in speed detection, suggests that local (orientation) and global (motion) information is already linked within V1. We propose the existence of a "dynamic association field" in V1 neurons, whose spatial extent and anisotropy are transiently updated and reshaped as a function of changes in the retinal flow statistics imposed during natural oculomotor exploration. SIGNIFICANCE STATEMENT: The computational role of primary visual cortex in low-level perception remains debated. The expression of this "pop-out" perception is often assumed to require attention-related processes, such as top-down feedback from higher cortical areas. Using intracellular techniques in the anesthetized cat and novel analysis methods, we reveal unexpected structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. These structural-functional biases provide a substrate, within V1, for contour detection and, more unexpectedly, global motion flow sensitivity at saccadic speed, even in the absence of attentional processes. We argue for the concept of a "dynamic association field" in V1 neurons, whose spatial extent and anisotropy changes with retinal flow statistics, and more generally for a renewed focus on intracortical computation.

Hidden complexity of synaptic receptive fields in cat V1.

In the primary visual cortex (V1), Simple and Complex receptive fields (RFs) are usually characterized on the basis of the linearity of the cell spiking response to stimuli of opposite contrast. Whether or not this classification reflects a functional dichotomy in the synaptic inputs to Simple and Complex cells is still an open issue. Here we combined intracellular membrane potential recordings in cat V1 with 2D dense noise stimulation to decompose the Simple-like and Complex-like components of the subthreshold RF into a parallel set of functionally distinct subunits. Results show that both Simple and Complex RFs exhibit a remarkable diversity of excitatory and inhibitory Complex-like contributions, which differ in orientation and spatial frequency selectivity from the linear RF, even in layer 4 and layer 6 Simple cells. We further show that the diversity of Complex-like contributions recovered at the subthreshold level is expressed in the cell spiking output. These results demonstrate that the Simple or Complex nature of V1 RFs does not rely on the diversity of Complex-like components received by the cell from its synaptic afferents but on the imbalance between the weights of the Simple-like and Complex-like synaptic contributions.

Communication through resonance in spiking neuronal networks.

The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

The role of delayed suppression in slow and fast contrast adaptation in V1 simple cells.

The sensitivity and rate of neural coding along the early visual pathways adapt to changes in contrast of the retinal image caused by external motion or self-generated eye movements. To identify the functional mechanisms of fast and slow contrast adaptation at the level of the visual cortex, we randomly varied, over both short and long timescales, the contrast of optimal sinusoidal gratings flashed in the receptive field of simple cells. We found that fast contrast-dependent suppression lagged excitation by ~11 ms and controlled the spike's temporal precision. During slow adaptation to low contrasts, the gain and latency of excitation increased whereas suppression became less visible, resulting in more sensitive but slower and more variable responses. We conclude that delayed suppression controls the response dynamics during both fast and slow contrast adaptation. More generally, we propose that sensory adaptation trades neuronal sensitivity for processing speed by changing the balance between excitation and delayed inhibition.

"Master" neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task.

Operant control of a prosthesis by neuronal cortical activity is one of the successful strategies for implementing brain-machine interfaces (BMI), by which the subject learns to exert a volitional control of goal-directed movements. However, it remains unknown if the induced brain circuit reorganization affects preferentially the conditioned neurons whose activity controlled the BMI actuator during training. Here, multiple extracellular single-units were recorded simultaneously in the motor cortex of head-fixed behaving rats. The firing rate of a single neuron was used to control the position of a one-dimensional actuator. Each time the firing rate crossed a predefined threshold, a water bottle moved toward the rat, until the cumulative displacement of the bottle allowed the animal to drink. After a learning period, most (88%) conditioned neurons raised their activity during the trials, such that the time to reward decreased across sessions: the conditioned neuron fired strongly, reliably and swiftly after trial onset, although no explicit instruction in the learning rule imposed a fast neuronal response. Moreover, the conditioned neuron fired significantly earlier and more strongly than nonconditioned neighboring neurons. During the first training sessions, an increase in firing rate variability was seen only for the highly conditionable neurons. This variability then decreased while the conditioning effect increased. These findings suggest that modifications during training target preferentially the neuron chosen to control the BMI, which acts then as a "master" neuron, leading in time the reconfiguration of activity in the local cortical network.

Cortically-controlled population stochastic facilitation as a plausible substrate for guiding sensory transfer across the thalamic gateway.

The thalamus is the primary gateway that relays sensory information to the cerebral cortex. While a single recipient cortical cell receives the convergence of many principal relay cells of the thalamus, each thalamic cell in turn integrates a dense and distributed synaptic feedback from the cortex. During sensory processing, the influence of this functional loop remains largely ignored. Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. The synaptic bombardment of cortical origin was mimicked through the injection of a stochastic mixture of excitatory and inhibitory conductances, resulting in a gradable correlation level of afferent activity shared by thalamic cells. The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. This suggests that the transfer efficiency of relay cells could be selectively amplified when they become simultaneously desynchronized by the cortical feedback. When applied to the intact brain, this network regulation mechanism could direct an attentional focus to specific thalamic subassemblies and select the appropriate input lines to the cortex according to the descending influence of cortically-defined "priors".

Stable learning in stochastic network states.

The mammalian cerebral cortex is characterized in vivo by irregular spontaneous activity, but how this ongoing dynamics affects signal processing and learning remains unknown. The associative plasticity rules demonstrated in vitro, mostly in silent networks, are based on the detection of correlations between presynaptic and postsynaptic activity and hence are sensitive to spontaneous activity and spurious correlations. Therefore, they cannot operate in realistic network states. Here, we present a new class of spike-timing-dependent plasticity learning rules with local floating plasticity thresholds, the slow dynamics of which account for metaplasticity. This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and solve the problem of asymptotic stable learning in noisy states. It is shown to naturally encompass many other known types of learning rule, unifying them into a single coherent framework. The mixed presynaptic and postsynaptic dependency of the floating plasticity threshold is justified by a cascade of known molecular pathways, which leads to experimentally testable predictions.

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Horizontal connectivity in V1: Prediction of coherence in contour and motion integration.

This study demonstrates the functional importance of the Surround context relayed laterally in V1 by the horizontal connectivity, in controlling the latency and the gain of the cortical response to the feedforward visual drive. We report here four main findings: 1) a centripetal apparent motion sequence results in a shortening of the spiking latency of V1 cells, when the orientation of the local inducer and the global motion axis are both co-aligned with the RF orientation preference; 2) this contextual effects grows with visual flow speed, peaking at 150-250°/s when it matches the propagation speed of horizontal connectivity (0.15-0.25 mm/ms); 3) For this speed range, the axial sensitivity of V1 cells is tilted by 90° to become co-aligned with the orientation preference axis; 4) the strength of modulation by the surround context correlates with the spatiotemporal coherence of the apparent motion flow. Our results suggest an internally-generated binding process, linking local (orientation /position) and global (motion/direction) features as early as V1. This long-range diffusion process constitutes a plausible substrate in V1 of the human psychophysical bias in speed estimation for collinear motion. Since it is demonstrated in the anesthetized cat, this novel form of contextual control of the cortical gain and phase is a built-in property in V1, whose expression does not require behavioral attention and top-down control from higher cortical areas. We propose that horizontal connectivity participates in the propagation of an internal "prediction" wave, shaped by visual experience, which links contour co-alignment and global axial motion at an apparent speed in the range of saccade-like eye movements.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.

How Blue is the Sky?

The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an uncontrolled societal hype, the more amplified, the more "Blue Sky" the claim is. Will we ever be able to simulate a brain in silico? Will "it" (the digital avatar) be conscious? The Blue Brain Project (BBP) and the European flagship the Human Brain Project (HBP) have surfed on this wave for the past 10 years. Their already significant lifetimes now offer new case studies for neuroscience sociology and epistemology, as the projects mature. Their distinctive "Blue Sky" flavor has been a key feature in securing unprecedented funding (more than one billion Euros) mostly through supranational institutions. The longitudinal analysis of these ventures provides clues to how the neuromyth they propagate sells science, in a scientific world based on an economy of promises.

Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1.

The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 [Formula: see text]m, and light intensity as weak as [Formula: see text] photons/s/cm[Formula: see text] is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.

Reliable recall of spontaneous activity patterns in cortical networks.

Irregular ongoing activity in cortical networks is often modeled as arising from recurrent connectivity. Yet it remains unclear to what extent its presence corrupts sensory signal transmission and network computational capabilities. In a recurrent cortical-like network, we have determined the activity patterns that are better transmitted and self-sustained by the network. We show that reproducible spiking and subthreshold dynamics can be triggered if the statistics of the imposed external drive are consistent with patterns previously seen in the ongoing activity. A subset of neurons in the network, constrained to replay temporal pattern segments extracted from the recorded ongoing activity of the same network, reliably drives the remaining, free-running neurons to call the rest of the pattern. Comparison with surrogate Poisson patterns indicates that the efficiency of the recall and completion process depends on the similarity between the statistical properties of the input with previous ongoing activity The reliability of evoked dynamics in recurrent networks is thus dependent on the stimulus used, and we propose that the similarity between spontaneous and evoked activity in sensory cortical areas could be a signature of efficient transmission and propagation across cortical networks.

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

Involvement of nicotinic and muscarinic receptors in the endogenous cholinergic modulation of the balance between excitation and inhibition in the young rat visual cortex.

This study aims to clarify how endogenous release of cortical acetylcholine (ACh) modulates the balance between excitation and inhibition evoked in visual cortex. We show that electrical stimulation in layer 1 produced a significant release of ACh measured intracortically by chemoluminescence and evoked a composite synaptic response recorded intracellularly in layer 5 pyramidal neurons of rat visual cortex. The pharmacological specificity of the ACh neuromodulation was determined from the continuous whole-cell voltage clamp measurement of stimulation-locked changes of the input conductance during the application of cholinergic agonists and antagonists. Blockade of glutamatergic and gamma-aminobutyric acid (GABAergic) receptors suppressed the evoked response, indicating that stimulation-induced release of ACh does not directly activate a cholinergic synaptic conductance in recorded neurons. Comparison of cytisine and mecamylamine effects on nicotinic receptors showed that excitation is enhanced by endogenous evoked release of ACh through the presynaptic activation of alpha(*)beta4 receptors located on glutamatergic fibers. DHbetaE, the selective alpha4beta2 nicotinic receptor antagonist, induced a depression of inhibition. Endogenous ACh could also enhance inhibition by acting directly on GABAergic interneurons, presynaptic to the recorded cell. We conclude that endogenous-released ACh amplifies the dominance of the inhibitory drive and thus decreases the excitability and sensory responsiveness of layer 5 pyramidal neurons.

[Multiscale functional imaging: reconstructing network dynamics from the synaptic echoes recorded in a single visual cortex neuron]. / Imagerie fonctionnelle multi-echelle: reconstruction de la dynamique corticale a partir des echos synaptiques enregistrés dans un seul neurone.

In vivo intracellular electrophysiology offers the unique possibility of listening to the "synaptic rumor " of the cortical network, captured by a recording electrode in a single V1 cell. It allows one to reconstruct the distribution of input sources in space and time, i.e. the effective network dynamics. We have used a reverse engineering method to demonstrate the propagation of visually evoked activity through lateral (and feedback) connectivity in the primary cortex of higher mammals. This approach, based on synaptic echography, is compared here with a real-time brain imaging technique based on voltage-sensitive dye imaging. The former method gives access to the microscopic convergence processes of single neurons, whereas the latter describes the macroscopic divergence process on the neuronal map. A combination of the two techniques can be used to elucidate the cortical origin of low-level (non attentive) binding processes participating in the emergence of Gestalt percepts.

Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning.

This intracellular study investigates synaptic mechanisms of orientation and direction selectivity in cat area 17. Visually evoked inhibition was analyzed in 88 cells by detecting spike suppression, hyperpolarization, and reduction of trial-to-trial variability of membrane potential. In 25 of these cells, inhibition visibility was enhanced by depolarization and spike inactivation and by direct measurement of synaptic conductances. We conclude that excitatory and inhibitory inputs share the tuning preference of spiking output in 60% of cases, whereas inhibition is tuned to a different orientation in 40% of cases. For this latter type of cells, conductance measurements showed that excitation shared either the preference of the spiking output or that of the inhibition. This diversity of input combinations may reflect inhomogeneities in functional intracortical connectivity regulated by correlation-based activity-dependent processes.