Theoretical and Computational Insights into Brain-Based Cognition

Understanding how the brain encodes, processes, and transforms information to give rise to cognition is a central challenge in neuroscience. Recent advances in computational modeling, neuroimaging, and neural data analysis have enabled researchers to bridge theoretical frameworks with empirical observations, offering novel insights into the mechanisms underlying perception, memory, attention, and decision-making. By integrating mathematical models with biological data, computational neuroscience provides powerful tools to simulate and predict cognitive functions across multiple levels of brain organization—from single neurons to large-scale networks.

Despite significant progress in neuroscience, a comprehensive understanding of how the brain encodes and transforms information to support complex cognition remains elusive. One core challenge lies in linking empirical observations often high-dimensional and noisy—to theoretical models that can accurately capture the dynamics and computations of neural systems. Traditional approaches often fall short in bridging this gap, limiting our ability to interpret neural data in cognitive terms. However, recent advances in large-scale neural recording, machine learning, and neurocomputational modeling have opened new avenues for unifying empirical and theoretical perspectives. Techniques such as recurrent neural networks, graph theory, and Bayesian inference now allow researchers to build models that not only fit data but also generate testable predictions. This Research Topic seeks to promote contributions that develop or apply such approaches to dissect cognitive functions, fostering a deeper understanding of how information processing in the brain gives rise to perception, memory, language, and decision-making.

This Research Topic invites contributions that explore the neural basis of information processing and cognition through both theoretical and empirical lenses. Specifically, studies that develop or apply computational models to understand cognitive functions such as perception, attention, memory, decision-making, learning, and language. Relevant themes include neural coding, network dynamics, brain-inspired algorithms, predictive processing, and information theory in neuroscience. Submissions that integrate neuroimaging, electrophysiology, or behavioral data with computational frameworks are particularly encouraged. We are also interested in manuscripts that use machine learning to uncover structure in neural data or test cognitive theories.

Don't miss the chance to make your manuscript contribution and showcase your research alongside the work of other outstanding colleagues. Register your interest in the link below so our Topic Editors can make sure to extend deadlines for your submission if needed: Participate in this topic