Frontiers in Computational Neuroscience (Oct 2014)
Dynamic stability of sequential stimulus representations in adapting neuronal networks
Abstract
The ability to acquire and maintain appropriate representations of time-varying, sequentialstimulus events is a fundamental feature of neocortical circuits and a necessary first step towardsmore specialized information processing. The dynamical properties of such representationsdepend on the current state of the circuit, which is determined primarily by the ongoing, internallygenerated activity, setting the ground state from which input-specific transformations emerge.Here, we begin by demonstrating that timing-dependent synaptic plasticity mechanisms havean important role to play in the active maintenance of an ongoing dynamics characterized byasynchronous and irregular firing, closely resembling cortical activity in vivo. Incoming stimuli,acting as perturbations of the local balance of excitation and inhibition, require fast adaptiveresponses to prevent the development of unstable activity regimes, such as those characterizedby a high degree of population-wide synchrony. We establish a link between such pathologicalnetwork activity, which is circumvented by the action of plasticity, and a reduced computationalcapacity. Additionally, we demonstrate that the action of plasticity shapes and stabilizes thetransient network states exhibited in the presence of sequentially presented stimulus events,allowing the development of adequate and discernible stimulus representations. The mainfeature responsible for the increased discriminability of stimulus-driven population responsesin plastic networks is shown to be the decorrelating action of inhibitory plasticity and theconsequent maintenance of the asynchronous irregular dynamic regime both for ongoing activityand stimulus-driven responses, whereas excitatory plasticity is shown to play only a marginalrole.
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