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Résumé |
Understanding the biophysical mechanisms underlying the dynamics of cortical
circuits in the brain is one of the major challenges in neuroscience.
Electrophysiological recordings show that the cerebral cortex is characterized
by irregular neural activity, which manifests itself in the high temporal
variability of spiking and the broad distribution of firing rates. Theoretical
works have shown that irregular activity emerges dynamically in network models
if the coupling between cells is strong, i.e. if the mean number of synaptic
connections per neuron K is large and synaptic strength is of order 1/\sqrt{K}.
However, the degree to which these models capture the mechanisms underlying
neuronal firing in cortical circuits is not fully understood. In particular,
results have been derived using simplified neuron models, characterized by
current-based synapses, and an understanding of how irregular firing emerges in
more biologically realistic neuron models is still lacking.
In this talk, I will discuss biophysical mechanisms shaping the dynamics of
neural activity in cortical circuits. First I will show that, in network models
with conductance-based synapses, irregular firing emerges if synaptic strength
is of order 1/\log(K) and, unlike in current-based models, persists even under
the large heterogeneity of connections that has been reported experimentally. I
will then discuss how dynamical stability can be implemented in cortical
circuits and provide experimental evidence of the critical role played by
recurrent inhibition in stabilizing neural dynamics in the mouse cortex. I will
conclude by discussing the computational consequences of these results for how
cortical circuits process sensory information. |
