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...
The Journal of neuroscience : the official journal of the Society for Neuroscience
Aug 6, 2025
Identification of the neuron type is critical when using extracellular recordings in awake, behaving animal subjects to understand computation in neural circuits. Yet, modern recording probes have limited power to resolve neuron identity. Here, we pr...
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...
Brain networks exhibit diverse topological structures to adapt and support brain functions. The changes in neuronal network architecture can lead to alterations in neuronal spiking activity, yet how individual neuronal behavior reflects network struc...
Closed-loop brain-computer interfaces can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks can be bidirectional...
OBJECTIVE: Bupropion, a norepinephrine-dopamine reuptake inhibitor, is widely used as an antidepressant and smoking cessation aid. At high doses, it also inhibits pancreatic β-cell ATP-sensitive potassium (KATP) channels, inducing insulin secretion. ...
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fas...
Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN co...
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits represent a promising approach for implementing the brain's comp...
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location wit...
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