Structural Plasticity and Associative Memory in Balanced Neural Networks With Spike-Time Dependent Inhibitory Plasticity
Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes and the effects of the altered connectivity on network function are yet to be explained. Experimental evidence suggests that neuronal activity modulates neurite morphology and that it may stimulate neurites to selectively sprout or retract to restore network activity levels. In this study, a new spiking network model was developed to investigate these activity dependent growth regimes of neurites. Simulations of the model accurately reproduce network rewiring after peripheral lesions as reported in experiments. To ensure that these simulations closely resembled the behaviour of networks in the brain, a biologically realistic network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex was deafferented. Furthermore, to study the functional effects of peripheral lesioning and subsequent network repair by homeostatic structural plasticity, associative memories were stored in the network and their recall performances before deafferentation and after, during the repair process, were compared. The simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. In this model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Furthermore, it was observed that the proposed model of homeostatic structural plasticity and the inhibitory synaptic plasticity mechanism that also balances the AI network are both necessary for successful rewiring. Next, even though average activity was restored to deprived neurons, these neurons did not retain their AI firing characteristics after repair. Finally, the recall performance of associative memories, which deteriorated after deafferentation, was not restored after network reorganisation.
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