Modeling cognition through adaptive neural synchronization: a multimodal framework using EEG, fMRI, and reinforcement learning.

ABSTRACT:

Introduction

Understanding the cognitive process of thinking as a neural phenomenon remains a central challenge in neuroscience and computational modeling. This study addresses this challenge by presenting a biologically grounded framework that simulates adaptive decision making across cognitive states.

Methods

The model integrates neuronal synchronization, metabolic energy consumption, and reinforcement learning. Neural synchronization is simulated using Kuramoto oscillators, while energy dynamics are constrained by multimodal activity profiles. Reinforcement learning agents-Q-learning and Deep Q-Network (DQN)-modulate external inputs to maintain optimal synchrony with minimal energy cost. The model is validated using real EEG and fMRI data, comparing simulated and empirica

SUBMITTER: Hall R 

PROVIDER: S-EPMC12571814 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature