Cognisciences: Synaptic integration and functional plasticity in primary visual cortex

Leader

Research center

1 avenue de la Terrasse1 avenue de la Terrasse
91190 Gif-sur-Yvette
Yves Frégnac

Institution

CNRS
Université Paris Sud
ED158 - 3C
Université Pierre et Marie Curie

Laboratory

Phone: +33(0)169823429
Fax: +33(0)169823427
UPR 3293
Idex i-Code, Idex NeuroSaclay

Keywords

Vision
electrophysiology
imaging
Multiscale
Modeling
Available to host a PhD student

Publications

Gerard-Mercier F, Carelli PV, Pananceau M, Troncoso XG, Frégnac Y. Synaptic Correlates of Low-Level Perception in V1. J Neurosci. 2016 Apr 6;36(14):3925-42. doi: 10.1523/JNEUROSCI.4492-15.2016.

Fournier J, Monier C, Levy M, Marre O, Sári K, Kisvárday ZF, Frégnac Y. Hidden complexity of synaptic receptive fields in cat V1. J Neurosci. 2014 Apr 16;34(16):5515-28. doi: 10.1523/JNEUROSCI.0474-13.2014.

Arduin P.J. , Ego-Stengel V. , Frégnac Y. and Shulz D.E. (2013) Master neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task. The Journal of Neuroscience. 33(19): 8308-20.

Levy M. , Fournier J. and Frégnac Y. (2013) The role of delayed suppression in slow and fast contrast adaptation in v1 simple cells. The Journal of Neuroscience. 33(15):6388-400.

El Boustani S. , Yger P. , Destexhe A. and Frégnac Y. (2012) A. Stable learning in stochastic network states. J. Neurosci. 32: 194-214.

Frégnac Y. (2012) Reading out the synaptic echoes of low level perception in V1. Lecture Notes in Computer Science. 7583: 486-495.

Chavane F. , Sharon D. , Jancke D. , Marre O. , Frégnac Y. and Grinvald A. (2011) Lateral spread of orientation selectivity in V1 is controlled by intracortical cooperativity. Frontiers in System Neuroscience, 5:4. 1-26.

Fields of research

Neurophysiology / systems neuroscience

Research Theme

An issue central to the theme of complexity in the dynamics and function of biological networks concerns the inferences that can be made from multiscale measurements ranging from microscopic to macroscopic levels of integration. Our research is based on interdisciplinary approaches ranging from electrophysiology (intracellular sharp and patch recordings, in vivo), network imaging (voltage sensitive dye), psychophysical measurements to functional databasing and phenomenological and computational modeling. The long-term aim is to relate elementary processes of integration (conductance activation) to the emergence of collective « high-order » network properties expressed during low-level (non-attentive) perception.The various research axes are centered on the study of complexity in the dynamics of neocortical networks during sensory processing and percept formation, as well as during functional adaptation and plasticity:

- At the experimental level, reverse engineering techniques will be developed to extrapolate, from the synaptic echoes recorded intracellularly in a single cell, the dynamics of the effective cortical network in which this recorded cell is functionally embedded. Dynamic clamp techniques will be developed with Thierry Bal’s team to connect biological and artificial neurons, in real time in vivo. Multiscale space-frequency-time analyses will be performed on simultaneously recorded microscopic (single cell Vm, synaptic conductances) and macroscopic (VSD imaging, EEG) signals during various levels of anesthesia. A collaboration is under way with Shulz’s team to explore neuroprosthetic applications.

– At the theoretical level, computer-based simulations will be used in two ways : i) neuroinformatics : an integrated database pooling a ten-year period of electrophysiological exploration is developed in collaboration with Andrew Davison’s team, and will serve to generate structural and functional models obtained through international collaborations (Ad Aertsen, Anders Lansner, Wolfgang Maass, Guillaume Masson, all partners in a European integrated project (Facets)); ii) computational neuroscience : large-scale numerical simulations are used to test general algorithms of associative plasticity and predict the dynamic behaviour of constrained recurrent networks, working near the edge of a deterministic chaos.

Our work should open new perspectives on the concept of « ongoing activity » in neural networks, and more specifically « coding efficiency » in sensory neocortex.