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
