The concept of logical neural networks, proposed by McCulloch and Pitts, along with Hebb's postulate of learning-specifically, spike-timing-dependent plasticity (STDP), has had a substantial influence on the development of brain-inspired computing re...
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 ...
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...
Human intelligence arises from the interplay between a compliant morphology and a cognitive system that is capable of adaptive learning. Soft robots exhibit similar mechanical compliance, but they still need learning capabilities that can be generali...
Neural-tumor electrophysiology-marked by pathological membrane potentials and ion channel dysregulation-emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functio...
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...
Recent advancements in spiking neural networks (SNNs) have drawn inspiration from the human brain's distinctive capabilities, leading to significant impacts on various aspects of our lives and scientific endeavors. The development of hardware-based S...
Accurate classification of neuronal cell types is essential for understanding brain organization, but multimodal neuron datasets are scarce and strongly imbalanced across subclasses. We present a benchmark of synthetic data augmentation methods for p...
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...
Proceedings of the National Academy of Sciences of the United States of America
Dec 10, 2025
Social information processing involves coordinated neural activity across distributed brain circuits, with the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) playing pivotal roles. However, whether these regions employ distinct coding...
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