TL;DR
The proposed design enables dynamic adjustment of delays according to practical requirements, maintaining low hardware overhead while providing precise and flexible temporal control, thus offering a foundation for programmable, efficient, and temporally aware neuromorphic systems.
A high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics and paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
This work introduces their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents, and derives and validate a mean-field neural mass model that remains stable while retaining working-memory functionality.
This work proposes a network downscaling methodology utilizing a 4-prong objective function and demonstrates a small-scale CA3-inspired network with 179 Izhikevich-modeled neurons, 3 neuronal types and 17,996 synapses with similar resting-state dynamics as the orders-of-magnitude larger full-scale network.
Neuromorphic spiking neural networks (SNNs) offer a promising alternative to conventional deep neural networks for tasks with computational resource or data constraints. However, their practical applications have been limited by comparatively weak performance on complex learning tasks. Experimental approaches such as S...
Findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
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