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Optimality among optimals: decision making from neurobiological ‘noisy’ signals within the cortico-basal-ganglia system

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If you have a question about this talk, please contact Prof Máté Lengyel .

The basal ganglia, a group of sub-cortical nuclei, have been implicated in decision making in a variety of contexts ranging from sensory-motor functions to working memory, learning and emotions (Redgrave et al, 1999). Recently the cortico-basal-ganglia system (CBGS) has been proposed to be performing such allocation of resources in an optimal fashion (Bogacz & Gurney, 2007), through a quasi-Bayesian algorithm known as multi-hypothesis sequential probability ratio test (MSPRT). Hitherto, decision making models, including that used by Bogacz & Gurney, have typically considered inputs with Gaussian (allowing negative going signals) or discrete distributed noise (e.g. Poisson). However, we know that neurobiological signals are continuously distributed and always take positive values. To address this problem we have shown how the MSPRT and CBGS may accommodate non-Gaussian continuous distributions that better describe the neural data. We showed that, all provided with the same conditions, our MSPR Ts reduce the sample size required to reach a decision, by at least one order of magnitude, compared to the best previous published model. This as the computational side of it, however we also showed that these new models do so while reproducing the trends in the neurobiological recordings available and preserving their mapping onto the CBGS . In this talk I will explain the development of our project, the results and where we are heading now.

This talk is part of the Computational Neuroscience series.

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