PT - JOURNAL ARTICLE
AU - Volo, Matteo di
AU - Romagnoni, Alberto
AU - Capone, Cristiano
AU - Destexhe, Alain
TI - Biologically realistic mean-field models of conductancebased networks of spiking neurons with adaptation
AID - 10.1101/352393
DP - 2018 Jan 01
TA - bioRxiv
PG - 352393
4099 - http://biorxiv.org/content/early/2018/08/25/352393.short
4100 - http://biorxiv.org/content/early/2018/08/25/352393.full
AB - Accurate population models are needed to build very large scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of Adaptive exponential Integrate and fire excitatory and inhibitory neurons. Using a Master Equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics. The mean-field model is capable to correctly predict the average spontaneous activity levels in asynchronous irregular regimes similar to in vivo activity. It also captures the transient temporal response of the network to complex external inputs. Finally, the mean-field model is also able to quantitatively describe regimes where high and low activity states alternate (UP-DOWN state dynamics), leading to slow oscillations. We conclude that such mean-field models are “biologically realistic” in the sense that they can capture both spontaneous and evoked activity, and they naturally appear as candidates to build very large scale models involving multiple brain areas.