A recurrent neural network fitted to large electrophysiological datasets may help us understand the chain of cortical information transmission. In particular, successful network reconstruction methods should enable a model to predict the response to ...
Proceedings of the National Academy of Sciences of the United States of America
Jan 9, 2026
The human brain excels at complex tasks with remarkable efficiency, adaptability, and resilience, making it a powerful source of inspiration for AI. Here, we present a neural dynamics model inspired by the primate dorsal visual pathway, a circuit cru...
This study addresses the important question of how neuron model choice and learning rules shape the classification performance of Spiking Neural Networks (SNNs) in bio-signal processing. By systematically contrasting Leaky Integrate-and-Fire, metaneu...
Foundation models have demonstrated transformative potential in medical artificial intelligence but remain underexplored in functional neuroimaging, particularly magnetoencephalography (MEG). This study aims to develop a domain-specific, self-supervi...
The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train...
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jan 7, 2026
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments (e.g., all-optical interrogation, closed-loop control) for interrogating neural circuits. Unfortunately, these a...
Proceedings of the National Academy of Sciences of the United States of America
Dec 12, 2025
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-based reinforcement learning, re...
Recurrent neural networks (RNNs) have emerged as a prominent tool for modeling cortical function. However, their conventional architecture is fundamentally lacking in physiological and anatomical fidelity, often raising questions regarding the validi...
Biomechanics and modeling in mechanobiology
Dec 12, 2025
Large-scale axonal dynamic simulation is critical to study white matter injury but is prohibitive in computational cost. We solve this challenge by training a convolutional neural network (CNN) that takes fiber strain profiles as inputs to instantly ...
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational ...
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