Romagnoni, A. | Search bioRxiv

bioRxiv2018

Mean-field model for the dynamics of conductance-based networks of excitatory and inhibitory spiking neurons with adaptation

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.

neuroscience↗

bioRxiv2018

Development of inhibitory synaptic delay drives maturation of thalamocortical network dynamics

Nervous system maturation occurs on multiple levels, synaptic, circuit, and network, at divergent time scales. For example, many synaptic properties mature gradually, while emergent network dynamics, as data show, change abruptly. Here, we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous thalamocortical activity necessary for the development of vision. Inspired by in vivo measurements of time-scales and amplitudes of synaptic currents, we extend the Wilson and Cowan model to take into account the relative onset timing and amplitudes of inhibitory and excitatory neural population responses. We study the dynamics of this system and identify the bifurcations as the onset timescales of excitation and inhibition are varied. We focus on the specific typical developmental changes in synaptic timescales consistent with the experimental observations. These findings argue that the inhibitory timing is a critical determinant of thalamocortical activity maturation; a gradual decay of the ratio of inhibitory to excitatory onset time below one drives the system through a bifurcation that leads to a sudden switch of the network spontaneous activity from high-amplitude oscillations to a non-oscillatory active state. This switch also drives a marked change to a linear network response to transient stimuli, agreeing to the in vivo observations. The switch observed in the model is representative of the sudden transition in the sensory cortical activity seen early in development.

neuroscience↗