Intracellular action potential (AP) recording that allows long-term monitoring is challenging because permanent membrane penetration is impossible due to cell death or resealing of perforated cell membrane. Herein, an "inherited noninvasive intracell...
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...
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...
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...
Neuronal ensemble activity, including coordinated and oscillatory patterns, exhibits hallmarks of nonequilibrium systems with time-asymmetric trajectories to maintain their organization. However, assessing time asymmetry from neuronal spiking activit...
Proceedings of the National Academy of Sciences of the United States of America
Nov 21, 2025
Information processing in the brain relies on the transmission of spikes through chemical synapses whose efficacies often depend on their recent firing history. While effects of such short-term plasticity on neural information processing have long be...
The success of deep learning methods over the past decade has been partially shrouded in the shadow of adversarial attacks. Even a tiny undetectable deformation can lead to vicious misleading targeted at safety-critical applications. In contrast, the...
Spiking Neural Networks (SNNs), designed to more accurately model the brain's neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational a...
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