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...
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...
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...
Proceedings of the National Academy of Sciences of the United States of America
Nov 4, 2025
Human neural networks of interconnected neurons have evolved to be remarkably efficient and are capable of learning and memory through the brain's synaptic plasticity, including short-term plasticity (STP), and long-term potentiation (LTP) and depres...
Proceedings of the National Academy of Sciences of the United States of America
Oct 31, 2025
Memory consolidation refers to a process of engram reorganization and stabilization that is thought to occur primarily during sleep through a combination of neural replay, homeostatic plasticity, synaptic maturation, and pruning. From a computational...
Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that encode differential (i.e., incremental) changes to a ...
Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG ...
Recently, technology has evolved significantly in the rehabilitation process for neurological disorders and neurodegenerative diseases, focusing on neuroplasticity. Neuroplasticity, as a fundamental base of brain rehabilitation, is the change in the ...
Neural networks face persistent challenges in maintaining stability and robustness during training, particularly in noisy or high-dimensional domains like molecular analysis. Inspired by biological neural systems that leverage homeostasis and self-re...
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent net...
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