<ul data-eligibleForWebStory="true"><li>Decision-making tasks in cognition are commonly modeled using Drift Diffusion Models (DDMs) and Poisson counter model.</li><li>These models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories.</li><li>A proposal for a Spiking Neural Network (SNN) model for decision-making is made to bridge the gap between cognitive and biological models.</li><li>The SNN model incorporates a learning mechanism and neuron activities are modeled by a multivariate Hawkes process.</li><li>A coupling result between DDM and the Poisson counter model is shown, indicating similar categorizations and reaction times.</li><li>The DDM can be approximated by spiking Poisson neurons.</li><li>A particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule.</li><li>An online categorization task was designed to evaluate the model predictions.</li><li>The work aims to integrate biologically relevant neural mechanisms into cognitive models for a deeper understanding of neural activity and behavior.</li></ul>

Spiking Neural Models for Decision-Making Tasks with Learning

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