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Article Dans Une Revue Physical Review E Année : 2019

State-dependent mean-field formalism to model different activity states in conductance based networks of spiking neurons

Résumé

Higher and higher interest has been shown in the recent years to large scale spiking simulations of cerebral neuronal networks, coming both from the presence of high performance computers and increasing details in the experimental observations. In this context it is important to understand how population dynamics are generated by the designed parameters of the networks, that is the question addressed by mean field theories. Despite analytic solutions for the mean field dynamics has already been proposed for current based neurons (CUBA), a complete analytic description has not been achieved yet for more realistic neural properties, such as conductance based (COBA) network of adaptive exponential neurons (AdEx). Here, we propose a novel principled approach to map a COBA on a CUBA. Such approach provides a state-dependent approximation capable to reliably predict the firing rate properties of an AdEx neuron with non-instantaneous COBA integration. We also applied our theory to population dynamics, predicting the dynamical properties of the network in very different regimes, such as asynchronous irregular (AI) and synchronous irregular (SI) (slow oscillations, SO). This results show that a state-dependent approximation can be successfully introduced in order to take into account the subtle effects of COBA integration and to deal with a theory capable to correctly predict the activity in regimes of alternating states like slow oscillations.
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Dates et versions

hal-02539198 , version 1 (09-04-2020)

Identifiants

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Cristiano Capone, Matteo Di Volo, Alberto Romagnoni, Maurizio Mattia, Alain Destexhe. State-dependent mean-field formalism to model different activity states in conductance based networks of spiking neurons. Physical Review E , 2019, 100, pp.062413. ⟨10.1103/PhysRevE.100.062413⟩. ⟨hal-02539198⟩
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