Biology subjects

Chemla, S.

Publications and source records attributed to Chemla, S..

5 recordsLinked to original sources

bioRxiv2025

Cortical Origins of the Flash-Lag Effect distortions: The Influence of Retinotopic Map Architecture

The flash-lag effect (FLE) is an illusion whereby the position of a moving object is perceived as being offset in the direction of movement when compared to a flashed static stimulus. This perceptual misalignment has been posited as a key phenomenon in explaining our ability to accurately predict the future position of moving objects, despite the delays in neuronal processing. Our working hypothesis is that the FLE is resulting from the anticipation generated by propagation of neural activity within visual cortical retinotopical maps. According to this hypothesis, the FLE should be affected by discontinuities and anisotropies of the retinotopic map architecture. Using psychophysics we show that the FLE is strongly affected by crossing and the direction of motion in respect to retinotopic features, such as vertical and horizontal meridians and the fovea. The specificity of how early visual cortical retinotopic maps are splitted and magnified around these features led us to suggest that the FLE distortions emerge from propagation in retinotopically organized networks, particularly V1. This work bridges the gap between human psychophysics and the known constraints of retinotopic maps layout, offering a testable framework for future studies of motion position encoding in the visual hierarchy.

neuroscience↗

bioRxiv2020

Anticipatory responses along motion trajectories in awake monkey area V1

What are the neural mechanisms underlying motion integration of translating objects? Visual motion integration is generally conceived of as a feedforward, hierarchical, information processing. However, feedforward models fail to account for many contextual effects revealed using natural moving stimuli. In particular, a translating object evokes a sequence of transient feedforward responses in the primary visual cortex but also propagations of activity through horizontal and feedback pathways. We investigated how these pathways shape the representation of a translating bar in monkey V1. We show that, for long trajectories, spiking activity builds-up hundreds of milliseconds before the bar enters the neurons receptive fields. Using VSDI and LFP recordings guided by a phenomenological model of propagation dynamics, we demonstrate that this anticipatory response arises from the interplay between horizontal and feedback networks driving V1 neurons well ahead of their feedforward inputs. This mechanism could subtend several perceptual contextual effects observed with translating objects. HighlightsO_LIOur hypothesis is that lateral propagation of activity in V1 contributes to the integration of translating stimuli C_LIO_LIConsistent with this hypothesis, we find that a translating bar induces anticipatory spiking activity in V1 neurons. C_LIO_LIA V1 model describes how this anticipation can arise from inter and intra-cortical lateral propagation of activity. C_LIO_LIThe dynamic of VSDi and LFP signals in V1 is consistent with the predictions made by the model. C_LIO_LIThe intra-cortical origin is further confirmed by the fact that a bar moving from the ipsilateral hemifield does not evoke anticipation. C_LIO_LIHorizontal and feedback input are not only modulatory but can also drive spiking responses in specific contexts. C_LI

neuroscience↗

bioRxiv2019

Revealing α oscillatory activity using voltage-sensitive dye imaging in monkey V1

The relevance of oscillations (7-12Hz) in neural processing, although recognized long ago, remains a major research question in the field. While intensively studied in humans, oscillations appear much less often investigated (and observed) in monkeys. Here we wish to provide data from non-human primates on stimulus-related rhythm. Indeed, in humans, EEG is enhanced in response to non-periodic dynamic visual stimulation ("perceptual echoes" or to a static stimulus ("flickering wheel illusion"). Do the same visual patterns induce an oscillatory response in monkey V1? We record voltage-sensitive dye signals from three anesthetized monkeys to investigate the population-based oscillatory neural response that is not resulting from attention-related feedback signals. We revealed oscillations in monkey V1 which, when they occur, react in a manner comparable to human studies.

neuroscience↗

bioRxiv2018

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↗

bioRxiv2017

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↗