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Summary 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., Highlights • 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, Stimuli suppress cortical correlated variability. Hennequin et al. show that a cortical operating regime of inhibitory stabilization around a single stable state—the “stabilized supralinear network”—explains this suppression’s tuning and timing, while alternative proposed regimes do not.

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

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

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

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

We include a recently described class of inhibitory cells, complex cells untuned for orientation, as well as simple cells in a model of V1 to study their contribution in shaping simple cell responses. Untuned complex cell inhibition can suffice to explain contrast-invariant orientation-tuning and low-pass temporal frequency tuning of cortical simple cells. Given this complex cell inhibition, antiphase (“push–pull”) inhibition from tuned simple inhibitory neurons acts to sharpen spatial frequency tuning and increase the stability of cortical activity. Intracortical inhibition is needed to achieve contrast-invariance of the voltage tuning, which is converted by physiological noise levels into contrast-invariant spike tuning.

Tetrodes allow isolation of multiple neurons at a single recording site by clustering spikes. Due to electrode drift and perhaps due to time-varying neuronal properties, positions and shapes of clusters change in time. As data is typically collected in sequential files, to track neurons across files one has to decide which clusters from different files belong to the same neuron. We report on a semi-automated neuron tracking procedure that uses computed similarities between the mean spike waveforms of the clusters. The clusters with the most similar waveforms are assigned to the same neuron, provided their similarity exceeds a threshold. To set this threshold, we calculate two distributions: of within-file similarities, and of best matches in the across adjacent file similarities. The threshold is set to the value that optimally separates the two distributions. We compare different measures of similarity (metrics) by their ability to separate these distributions. We find that these metrics do not differ drastically in their performance, but that taking into account the cross-channel noise correlation significantly improves performance of all metrics. We also demonstrate the method on an independent dataset and show that neurons, as assigned by the procedure, have consistent physiological properties across files.

The modulation of simple cell responses to low-temporal-frequency stimuli is suppressed by the superposition of high-temporal-frequency stimuli, while the modulation of the cell response to high-temporal-frequency stimuli is enhanced by superposition of low-temporal-frequency stimuli (Exp. Brain Res. 45 (1982) 456). Here, we show that correlation-based circuitry can account for these non-linear response properties. Our model predicts that the mean input to simple cells follows the tuning of the LGN, peaking at higher temporal frequencies than the cortical response. This increased mean input at higher frequencies, which is converted to net inhibition by the model circuit, is critical for explaining the superposition results.

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

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

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

We are exploring new methods of spike detection to improve spike-sorting in tetrode recordings. Based on our observation that the four channels of the tetrode carry highly correlated signals, we propose the use of a hyperellipsoidal thresholding surface in the four-dimensional space of the signal values to detect spikes. This surface is determined by the cross-channel covariance matrix and provides a better approximation of the equiprobable surface of the noise amplitude distribution compared to the traditionally used hypercubical thresholding surface. This spike detection procedure greatly improves the separation of signal clusters from the noise cluster around the origin. We have extended these approaches to automatic spike-sorting in both amplitude and full waveform spaces.

Healthy male subjects ingested 1.0 g ethanol/kg (Alcohol Day) and caloric equivalents of sucrose (Control Day). Plasma prolactin was determined on samples collected at 20-min intervals by serial constant blood exfusion, from 2 hr before to 4 hr after the drink. In 14 of the 15 men studied, plasma prolactin levels during the 2-hr period after alcohol administration were elevated an average of 31% above values for the preceding 2-hr period. Data pooled for all subjects revealed a small but statistically significant increase in prolactin coinciding with ascending and peak concentrations of blood alcohol. A significant increment in prolactin was associated with peak blood alcohol levels when values were compared between control and alcohol treatment days. Although of statistical significance, these transient and variable increases were within the normal range of basal prolactin levels for most subjects and are unlikely to be physiologically meaningful.