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Conference Papers Year : 2017

The art of scaling up : a computational account on action selection in basal ganglia

Abstract

What makes a computational neuronal model 'large scale' ? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across coordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explore modeling a neural system involving some areas of cortex, the basal ganglia (BG) and thalamus for the process of decision making, using a large-scale neural engineering framework, Nengo. Early results tend to replicate the known neural activity patterns as found in the previous action selection model by Guthrie et al. 2013, besides operating with a larger neuronal populations. The power of converting algorithms to efficiently weighed neural networks in Nengo (Stewart et al. 2009 and Bekolay et al. 2013) is exploited in this work. Crucial aspects in a computational model, like parameter tuning and detailed neural implementations, while moving from a simplistic to large-scale model, are studied.
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Dates and versions

hal-01534459 , version 1 (07-06-2017)

Identifiers

  • HAL Id : hal-01534459 , version 1

Cite

Bhargav Teja Nallapu, Bapi Raju Surampudi, Nicolas P. Rougier. The art of scaling up : a computational account on action selection in basal ganglia: [FULL Version]. IJCNN 2017 - International Joint Conference on Neural Networks, May 2017, Anchorage, Alaska, United States. ⟨hal-01534459⟩
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