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Integrated Spin model with global inhibition for decision making

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If you have a question about this talk, please contact Samuel Eckmann .

Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. Recently, it was found that during high cognitive load and situations of uncertainty, inhibition of neuronal firing increases, but the origin of this phenomenon is not understood. Motivated by this observation, we extend a recently developed model for decision-making while animals move towards targets in real space. We introduce an Ising-type model, which includes global inhibition, and explore its role in the decision-making process. This model can explain how the brain may utilize modulation of inhibition to improve its decision-making accuracy. Compared to experimental results, this model suggests that the regime of the brain’s decision-making activity is in proximity to a critical state. Within the model, the critical region near the transition line has the advantageous property of enabling a significant decrease in error with a small increase in inhibition and also exhibits unique properties with respect to learning and memory decay.

This talk is part of the Computational Neuroscience series.

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