  Computational models of cortical activity provide insight into the mechanisms
of higher-order processing in the human brain including planning, perception
and the control of movement. Activity in the cortex is ongoing even in the
absence of sensory input or discernible movements and is thought to be linked
to the topology of cortical circuitry. However, the connectivity and its
functional role in the generation of spatio-temporal firing patterns and
cortical computations are still unknown. Movement of the body is a key function
of the brain, with the motor cortex the main cortical area implicated in the
generation of movement. We built a spiking neural network model of the motor
cortex which incorporates a laminar structure and circuitry based on a previous
cortical model. A local connectivity scheme was implemented to introduce more
physiological plausibility to the cortex model, and the effect on the rates,
distributions and irregularity of neuronal firing was compared to the original
random connectivity method and experimental data. Local connectivity increased
the distribution of and overall rate of neuronal firing. It also resulted in
the irregularity of firing being more similar to those observed in experimental
measurements. The larger variability in dynamical behaviour of the local
connectivity model suggests that the topological structure of the connections
in neuronal population plays a significant role in firing patterns during
spontaneous activity. This model took steps towards replicating the macroscopic
network of the motor cortex, replicating realistic spatiotemporal firing to
shed light on information coding in the cortex. Large scale computational
models such as this one can capture how structure and function relate to
observable neuronal firing behaviour, and investigates the underlying
computational mechanisms of the brain.
