Structured networks support sparse traveling waves in rodent somatosensory cortex.

Neurons responding to different whiskers are spatially intermixed in the superficial layer 2/3 (L2/3) of the rodent barrel cortex, where a single whisker deflection activates a sparse, distributed neuronal population that spans multiple cortical columns. How the superficial layer of the rodent barrel cortex is organized to support such distributed sensory representations is not clear. In a computer model, we tested the hypothesis that sensory representations in L2/3 of the rodent barrel cortex are formed by activity propagation horizontally within L2/3 from a site of initial activation. The model explained the observed properties of L2/3 neurons, including the low average response probability in the majority of responding L2/3 neurons, and the existence of a small subset of reliably responding L2/3 neurons. Sparsely propagating traveling waves similar to those observed in L2/3 of the rodent barrel cortex occurred in the model only when a subnetwork of strongly connected neurons was immersed in a much larger network of weakly connected neurons.

Feedback stabilizes propagation of synchronous spiking in cortical neural networks.

Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation.

Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.

Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.

Perceptual priming leads to reduction of gamma frequency oscillations.

Oscillations of neural activity are ubiquitous in the brain and are critical for normal cognitive function. In the visual system, repetitive presentation of a stimulus results in the reduction of power elicited in the gamma frequency band. However, this reduction does not result in degradation of perception; on the contrary, perception is improved by prior experience with the stimulus. To explain how reduction of gamma frequency oscillations, observed in priming experiments, can lead to improvement in behavior, we assume that visual processing takes place in two distinct stages: representation sharpening in the early visual areas and competitive interaction among representations in the higher visual areas and the prefrontal cortex. Here, we present a network model of spiking neurons that demonstrates how stimulus repetition leads to a decrease in power of the local field potential oscillations in the gamma frequency range in the early layer and also improves network response by reducing the latency to reach a decision in the higher area.

Representation sharpening can explain perceptual priming.

Perceiving and identifying an object is improved by prior exposure to the object. This perceptual priming phenomenon is accompanied by reduced neural activity. But whether suppression of neuronal activity with priming is responsible for the improvement in perception is unclear. To address this problem, we developed a rate-based network model of visual processing. In the model, decreased neural activity following priming was due to stimulus-specific sharpening of representations taking place in the early visual areas. Representation sharpening led to decreased interference of representations in higher visual areas that facilitated selection of one of the competing representations, thereby improving recognition. The model explained a wide range of psychophysical and physiological data observed in priming experiments, including antipriming phenomena, and predicted two functionally distinct stages of visual processing.

A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization.

Experience with transient stimuli leads to stronger neural responses that also rise and fall more sharply in time. This sharpening enhances the processing of transients and may be especially relevant for speech perception. We consider a learning rule for inhibitory connections that promotes this sharpening effect by adjusting these connections to maintain a target homeostatic level of activity in excitatory neurons. We analyze this rule in a recurrent network model of excitatory and inhibitory units. Strengthening inhibitory-->excitatory connections along with excitatory-->excitatory connections is required to obtain a sharpening effect. Using the homeostatic rule, we show that repeated presentations of a transient signal will "teach" the network to respond to the signal with both higher amplitude and shorter duration. The model also captures reorganization of receptive fields in the sensory hand area after amputation or peripheral nerve resection.

Competitive dynamics in cortical responses to visual stimuli.

Neurons in the visual cortex of the macaque monkey exhibit a variety of competitive behaviors, including normalization and oscillation, when presented with multiple visual stimuli. Here we argue that a biophysically plausible cortical circuit with opponent inhibition, spike-frequency adaptation, and synaptic depression can account for the full range of behaviors. The governing parameter is the strength of inhibition between competing neuronal pools. As the strength of inhibition is increased, the pattern of network behavior shifts from normalization mode to oscillatory mode, with oscillations occurring at progressively lower frequency until, at the extreme, winner-take-all behavior appears.