Spiking neural models for decision-making tasks with learning.

Clinical Snapshot

Key Findings

• P Value: Not applicable - mathematical proof

• Effect Size: Not quantified - theoretical equivalence demonstrated

• Primary Outcome: Successful coupling between DDM and Poisson counter models with similar categorization and reaction time predictions

• Nnt Or Sensitivity: Model accuracy metrics not specified for categorization task

• Confidence Interval: Not reported

Clinical Application

Limited immediate clinical application; primarily research tool for understanding neural decision processes Relevant for Australian neuroscience research institutions and cognitive assessment development; potential future applications in neuropsychological testing Researchers studying decision-making, cognitive neuroscientists, and clinicians interested in neural mechanisms of choice behavior

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. This model 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.

References

1. 1.Jaffard, S., Mezzadri, G., Reynaud-Bouret, P., & Tanré, E. (2026). Spiking neural models for decision-making tasks with learning. Journal of Mathematical Biology. https://doi.org/10.3758/BF03194023