Spiking Neural Models for Decision-Making Tasks with Learning

2025-06-10

AI Summary

Existing decision-making models—such as the drift-diffusion model (DDM) and Poisson counters—lack learnability and rely on class priors, limiting their biological plausibility and ability to capture online neural decision dynamics. Method: We propose a biologically plausible, learnable spiking neural network (SNN) that formalizes decision formation as spike activity driven by a multivariate Hawkes process. Crucially, we rigorously derive a DDM with correlated noise directly from the Hawkes-based SNN dynamics and introduce local, prior-free synaptic learning rules enabling online classification. Contribution/Results: We prove that this SNN uniformly approximates DDM dynamics. Experiments reproduce key behavioral signatures—including reaction-time distributions and choice biases—demonstrating dual consistency between neural spiking activity and behavioral outputs. Our work establishes the first theoretical bridge between SNNs and classical cognitive decision models, unifying mechanistic neurocomputational principles with normative decision theory.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Learning Preferences or Rankings

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Lack of learning mechanisms in existing decision-making models
• Bridging cognitive and biological models with spiking neural networks
• Integrating neural activity and behavior understanding via Hawkes processes

Innovation

• Spiking Neural Network model for decision-making
• Multivariate Hawkes process for neuron activities
• Local learning rule for biologically plausible learning