Is cortical connectivity optimized for storing information? | Nature Neuroscience
Maximizing information storage in recurrent networks leads to connectivity matrices whose statistics reproduce experimentally observed features of the connectivity between pyramidal cells in cortex. These include a large fraction of potential synapses and an over-representation of bidirectionally connected pairs of neurons, as compared to random networks.
Subjects Cortex Network models Abstract Cortical networks are thought to be shaped by experience-dependent synaptic plasticity. Theoretical studies have shown that synaptic plasticity allows a network to store a memory of patterns of activity such that they become attractors of the dynamics of the network. Here we study the properties of the excitatory synaptic connectivity in a network that maximizes the number of stored patterns of activity in a robust fashion. We show that the resulting synaptic connectivity matrix has the following properties: it is sparse, with a large fraction of zero sy
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