RT Journal Article
SR Electronic
T1 Biologically realistic mean-field models of conductancebased networks of spiking neurons with adaptation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 352393
DO 10.1101/352393
A1 Volo, Matteo di
A1 Romagnoni, Alberto
A1 Capone, Cristiano
A1 Destexhe, Alain
YR 2018
UL http://biorxiv.org/content/early/2018/08/25/352393.abstract
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.