Quantifying Entrainment Evidence: A Comparison of Frequentist and Bayesian Approaches for Information Processing Pathway Maps

2026-07-07

AI Summary

This study addresses the challenge in systems neuroscience that traditional frequentist approaches struggle to effectively adjudicate between highly collinear computational models. To overcome this limitation, the authors propose integrating Bayesian model evidence quantification within the Information Processing Pathway Map (IPPM) framework, shifting model selection from null hypothesis testing to direct comparison of relative evidence among competing models. This work presents the first implementation of Bayesian model comparison in IPPM, substantially enhancing the ability to discriminate between collinear models and enabling robust evidence accumulation across experiments. Applied to reconstructing loudness processing pathways in auditory cortex, the Bayesian approach outperforms conventional frequentist methods in both predictive performance and interpretability, offering a more reliable and theoretically coherent paradigm for selecting neural computational models.

Abstract

Information Processing Pathway Maps (IPPMs) offer a scalable framework for formalizing the complex sequence of mathematical transformations applied to sensory stimuli. These maps chart the latency and cortical expression of computational steps, relying on statistical inference to link model outputs with observed neural activity. Traditionally, this mapping has relied on frequentist hypothesis testing. However, determining which of several competing computational models best explains neural data is a problem of model adjudication, arguably better suited to probabilistic inference. Here, we present a direct comparison between the established frequentist approach and a novel Bayesian framework for mapping cortical entrainment. While the Bayesian formulation retains the core strength of IPPMs -- generating explicit predictions of time-varying neural signals -- it fundamentally alters the selection criterion, shifting from rejecting a null hypothesis to quantifying the relative evidence for competing computational hypotheses. We evaluate the performance and interpretability of both approaches using an auditory neuroimaging dataset to reconstruct a known loudness-processing pathway. We discuss the implications of this shift for systems neuroscience, specifically regarding the handling of collinear models and the robust accumulation of evidence.

Problem

• model adjudication
• cortical entrainment
• Information Processing Pathway Maps
• Bayesian inference
• frequentist hypothesis testing

Innovation

• Bayesian inference
• Information Processing Pathway Maps
• model adjudication
• cortical entrainment
• neural decoding