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...
The nervous system flexibly processes information under different conditions. To do this, neural networks frequently rely on uniform expression of modulatory receptors by distinct classes of neurons to fine tune the computations supported by each neu...
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 18, 2025
The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits'...
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 ...
The corticospinal tract (CST) is essential for forelimb-specific fine motor skills. In rodents, it undergoes extensive structural remodeling across development, injury, and disease states, with major implications for motor function. A vast body of li...
Neuromorphic approaches to computation are driven by both the low-power operation of the biological brain and ever-increasing energy consumption of modern computing systems. Percolating networks of nanoparticles are promising candidates for self-asse...
Identifying the computational roles of different neuron families is crucial for understanding neural networks. Most neural diversity is embodied in various types of γ-aminobutyric acid-mediated (GABAergic) interneurons, grouped into four major famili...
Spiking neural networks (SNNs) are biologically more plausible and computationally more powerful than artificial neural networks due to their intrinsic temporal dynamics. However, vanilla spiking neurons struggle to simultaneously encode spatiotempor...
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