This project begun with Fabian Alvarez (postdoc in my group at UNIC) [1] and has been continued by Sami El Boustani [2,3] (PhD student in my group) and Pierre Yger [3] (PhD student in Fregnac's group) and consists of finding self-generated network states compatible with intracellular recordings during ``active states'' (desynchronized EEG) in neocortex. These states will then be used later for various computing paradigms. The aim was to obtain all possible network configurations that are compatible with given conductance measurements (excitatory and inhibitory) by studying self-consistency between inputs and output (firing rate, variability) in single cells. The predicted network configurations were then tested numerically to verify that active states are stable [1].
In a second approach, a macroscopic model of asynchronous irregular (AI) states was built. A mean-field approach was derived specifically for networks of excitatory and inhibitory spiking neurons in AI states. This approach successfully reproduced the complex state diagrams calculated numerically in networks of excitatory and inhibitory neurons [2]. This approach is presently pursued to analyze voltage-sensitive dye recordings in vivo (work in progress).
In a third approach, we considered networks of adaptative exponential integrate-and-fire neurons, which can simulate the diversity of intrinsic firing properties in neocortex [3]. Cortical and thalamocortical networks were considered, with network sizes ranging from small networks up to large networks. These networks reproduced many different in vivo like states, such as desynchronized states (AI states), or slow-wave oscillations with Up and Down states. Modulating spike frequency adaptation enabled the transition between these states. Both networks can be simulated on hardware ASIC neurons [4].
More recently, we considered networks of integrate-and-fire neurons with more realistic connectivity integrating the probability of connection found in cortical tissue [5]. These ``topological networks'' displayed different activity states very similar to random networks. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. This study suggests that the ``mean-field'' statistics of such networks does not depend on the details of the connectivity at a microscopic scale, suggesting that the mean-field formalism should apply to networks with realistic connectivity.
[1] Alvarez, F.P. and Destexhe, A. Simulating cortical network activity states constrained by intracellular recordings. Neurocomputing 58: 285-290, 2004 (see abstract)
[2] El Boustani, S. and Destexhe, A. A master equation formalism for macroscopic modeling of asynchronous irregular activity states. Neural Computation 21: 46-100, 2009 (see abstract)
[3] Destexhe, A. Self-sustained asynchronous irregular states and Up/Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. J. Computational Neurosci. 27: 493-506, 2009 (see abstract)
[4] Bruderle D, Petrovici MA, Vogginger B, Matthias Ehrlich M, Pfeil T, Millner S, Grubl A, Wendt K, Muller E, Schwartz M-O, Husmann de Oliveira D, Jeltsch S, Fieres J, Schilling M, Muller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zuhl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schuffny R, Schemmel J and Meier K. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol. Cybernetics 104: 263-296, 2011 (see abstract)
[5] Yger, P., El Boustani, S., Destexhe, A. and Frégnac, Y. Invariant macroscopic statistics in topologically-connected balanced networks of conductance-based integrate-and-fire neurons. J. Computational Neurosci. 31: 229-245, 2011 (see abstract)
Unité de Neurosciences, Information & Complexité
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