Sandrine Chemla

A biophysical cortical column model to study the multi-component origin of the VSDI signal

We propose a biological cortical column model, at an intermediate mesoscopic scale, in order to better understand and interpret biological sources of voltage-sensitive dye imaging signal (VSD signal). To perform a quantitative analysis of the relative contributions to the VSD signal, a detailed compartmental model was developed at a scale corresponding to one pixel of optical imaging. The generated model was used to solve the VSD direct problem, i.e. generate a VSD signal, given the neural substrate parameters and activities. Here, we confirm and quantify the fact that the VSD signal is the result of an average from multiple components. Not surprisingly, the compartments that mostly contribute to the signal are the upper layer dendrites of excitatory neurons. However, our model suggests that inhibitory cells, spiking activity and deep layers contributions are also significant and, more unexpected, are dynamically modulated with time and response strength.

Improving voltage-sensitive dye imaging: with a little help from computational approaches

Abstract. Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.

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

Summary How does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of motion. Large spatio-temporal separation challenges the visual system to keep track of object identity along the apparent motion path. Here, we utilize voltage-sensitive dye imaging in primary visual cortex (V1) of the awake monkey to investigate whether intra-cortical connections within V1 can shape cortical dynamics to represent the illusory motion. We find that the arrival of the second stimulus in V1 creates a suppressive wave traveling toward the retinotopic representation of the first. Computational approaches show that this suppressive wave can be explained by recurrent gain control fed by the intra-cortical network and contributes to precisely encode the expected motion velocity. We suggest that non-linear intra-cortical dynamics preformat population responses in V1 for optimal read-out by downstream areas.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.

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 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.

Anticipation of an approaching bar by neuronal populations in awake monkey V1

Visual motion integration in area V1 is traditionally investigated with local stimuli drifting over many cycles within a fixed aperture. However, psychophysical studies have suggested that motion signals can be optimally integrated along the trajectory of a single, translating dot. High detection performance can be explained by the propagation of information between adjacent detector units (Verghese et al., 1999). Such propagation mechanism was proposed to take place within the retinotopic cortical map in area V1 where each local input along the trajectory will elicit a spread of activity that can pre-activate future locations. To test this hypothesis, we recorded single-unit responses (n=80 cells) in area V1 of 2 fixating monkeys when presented with a small bar (4°) always drifting along the same direction (rightward, 6.6°/s) but with three different trajectory lengths (1.5, 3 and 6°). We found in 47% of the neurons an anticipatory build-up of spiking activity for long motion paths, starting as far as 2-4° from the RF center. This activity was not due to eye movements and was abolished when the order of the stimulus sequence was randomized. To probe the origin of such anticipatory responses, we recorded both LFP signals using multi-electrode-arrays and sub-threshold synaptic activity using voltage-sensitive-dye-imaging (VSDI). LFP responses showed a very early anticipatory signal that could be attributed to a fast feedback signal from higher areas. The dynamics of VSD sub-threshold anticipatory responses matched the spatiotemporal properties of the horizontal connectivity underlying propagation of neural activity within V1 retinotopic maps. Thus, anticipatory spiking response in V1 neurons is probably subtended by a combination of intra and inter-cortical signals converging onto V1 cells. These results highlight the complex, predictive integration of visual motion in primate area V1. Meeting abstract presented at VSS 2015.