Real-Time Neuromorphic Spectrum Intelligence Simulator

Evolving robust memristive spiking neural networks via metaheuristic algorithms for noise-resilient neuromorphic inference

Neuromorphic systems hold the promise of ultra-low-power event-conditions intelligence, but noise and non-idealities of hardware of the memristive devices limit their practical use. Nonetheless, it is not understood how to attain an inference which is both noise-robust and accurate as well as energy-efficient across re...

Schmitt Trigger Optimization for Neuromorphic Circuits

In neuromorphic computing, the performance of Spiking Neural Networks (SNNs) relies heavily on the precise firing threshold and reset voltages of its neurons. In Leaky Integrate-and-Fire (LIF) models for instance, these critical voltages are inherently governed by a hysteresis comparator. In this scenario, this paper p...

A memristor-based cerebellar PC–DCN circuit for robust neuromorphic signal integration in robotic applications

A minimal model of working memory in neural systems and neuromorphic circuits

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.

Metal‐Filamentary Memristor‐Based Artificial Neurons for Embodied Neuromorphic Intelligence

Embodied neuromorphic intelligence represents a transformative paradigm for next‐generation intelligent systems, with broad implications for robotics, autonomous driving, intelligent manufacturing, and smart infrastructures. Neuromorphic chips enable bioinspired computing with sparse, heterogeneous representations an...

Neuromorphic RISC-V Systems for Bio-inspiredComputing Applications

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.