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Chemla, S.

Publications and source records attributed to Chemla, S..

2 recordsLinked to original sources

Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1

The \"apparent motion\" illusion is evoked when stationary stimuli are successively flashed in spatially separated positions. It depends on the precise spatial and temporal separations of the stimuli. For large spatiotemporal separation, the long-range apparent motion (lrAM), it remains unclear how the visual system computes unambiguous motion signals. Here we investigated whether intracortical interactions within retinotopic maps could shape a global motion representation at the level of V1 population in response to a lrAM. In fixating monkeys, voltage-sensitive dye imaging revealed the emergence of a spatio-temporal representation of the motion trajectory at the scale of V1 population activity, shaped by systematic backward suppressive waves. We show that these waves are the expected emergent property of a recurrent gain control fed by the horizontal intra-cortical network. Such non-linearities explain away ambiguous correspondence problems of the stimulus along the motion path, preformating V1 population response for an optimal read-out by downstream areas.

neuroscience

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study here a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

neuroscience