Spiking Neural Models for Decision-Making Tasks with Learning

TL;DR

This paper introduces a biologically plausible spiking neural network model for decision-making that incorporates learning, bridging the gap between cognitive evidence accumulation models and neural mechanisms, and demonstrating its effectiveness through theoretical analysis and online experiments.

Contribution

It develops a novel SNN model with learning capabilities based on Hawkes processes, linking it to traditional decision-making models like DDM and Poisson processes.

Findings

The DDM can be approximated by spiking Poisson neurons.

A Hawkes network can derive a correlated noise DDM.

The model accurately predicts reaction times and choices in online tasks.

Abstract

In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decision as a stochastic process, specifically a Brownian motion, with the drift rate reflecting the strength of the evidence. In the same vein, the Poisson counter model describes the accumulation of evidence as discrete events whose counts over time are modeled as Poisson processes, and has a spiking neurons interpretation as these processes are used to model neuronal activities. However, these models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories. To bridge the gap between cognitive and biological models, we propose a biologically plausible Spiking Neural Network (SNN) model for decision-making that incorporates a learning mechanism and whose neurons activities are modeled by a multivariate Hawkes process. First, we show a coupling result between the DDM and the Poisson counter model, establishing that these two models provide similar categorizations and reaction times and that the DDM can be approximated by spiking Poisson neurons. To go further, we show that a particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule. In addition, we designed an online categorization task to evaluate the model predictions. This work provides a significant step toward integrating biologically relevant neural mechanisms into cognitive models, fostering a deeper understanding of the relationship between neural activity and behavior.