Simulating cortical network activity states constrained by intracellular recordings
We present a method for studying states of network activity while incorporating constraints provided by intracellular measurements. Taking into account measurements of the average membrane potential, input resistance changes and membrane potential fluctuations, narrows down the possible region of parameter space (connectivity, quantal conductances) where this activity can appear in networks. Searching in those specific regions greatly enhances the efficiency of the network-level modeling because irrelevant parameter combinations are automatically eliminated. We illustrate this approach by modeling self-sustained stochastic states in networks of excitatory and inhibitory neurons, based on intracellular recordings in vivo.
stochastic activity high-conductance states up-states network model