Phase offset between slow oscillatory cortical inputs influences competition in a model of the basal ganglia

Abstract

Abstract-Low-frequency oscillations have been the target of extensive research both in cortical structures and in the basal ganglia, due to numerous reports of associations with brain disorders and the normal functioning of the brain. Whereas a number of computational models of the basal ganglia investigate these phenomena, these models tend to focus on intrinsic oscillatory mechanisms, neglecting evidence that points to the cortex as the origin of this oscillatory behaviour. In this work we constructed a neural model of the basal ganglia circuitry and used it to investigate the relationship between frequency of oscillatory cortical input, dopamine and the effectiveness of the basal ganglia as an action selection device. Our simulations show the impact of the phase offset between different cortical inputs. This was found to be highly dependent on the frequency band and to have a strong influence on basal ganglia effectiveness. In addition, the level of dopamine in the system was found to modulate this effect, also depending on the input's frequency band.

A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington’s Disease

“…A detailed 27 and a subsequently simplified 28 computational model expressing the DA modulation in MSN D1 and D2 were proposed, and then added to a threedimensional network model together with fast spiking interneurons (FSI) in the striatum. 29 Other models studied the exploration/exploitation tradeoff, 8,30,61,62 or reproduced diverse behavioral tasks with a complex model containing several neuronal nuclei (cortex, BG and thalamus). 63 In some models, phasic DA signals have been added on top of a tonic DA value, reproducing the neural mechanism for which the triggering of a movement requires a dopaminergic burst just preceding the movement onset.…”

“…These effects have been modeled by extending the original Izhikevich's equations with one additional recovery variable (u 2 ). 62 The state variables are updated according to the following differential equations:…”