Frontiers in Computational Neuroscience (Oct 2011)
Inferring single neuron properties in conductance-based balanced networks
Abstract
Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. These states give rise to a generic behavior in which the individual dynamics of the neurons is almost irrelevant. We analyze the dynamical behavior of large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We find that Type II networks have a less robust balanced state because they tend to generate synchronized oscillations. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. These methods allow us to determine intrinsic properties of the neurons that are apparently masked by the balanced state. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise). We find a good agreement between the two procedures.
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