neuromorphic

A tiny memristor chip dramatically reduces brain modeling time to under 10 milliseconds, enabling faster neural simulations.

A new brain-inspired hardware design enables faster and more energy-efficient anomaly detection for AI systems, drawing on principles of neuromorphic computing.

A fully biocompatible multiterminal neuromorphic biodevice using quaternized chitosan is developed, operating at ultralow voltages and simulating visual nervous system processing.

This paper introduces morphologically tunable mycelium chips as a substrate for physical reservoir computing, leveraging the adaptive growth of fungal networks.

A brain-inspired AI architecture promises to deliver faster computing while consuming far less power, potentially advancing energy-efficient AI hardware.

Researchers develop a brain-inspired phototransistor that senses and stores data, potentially reducing AI energy consumption.

Otters++ is a novel optical spiking Transformer that leverages time-to-first-spike coding and physical hardware decay to achieve energy-efficient inference, achieving 84.17% on GLUE while maintaining a clear energy advantage over prior spiking Transformer baselines.

Jeff Bezos has funded Flourish, a neuro-AI startup valued at $2.5 billion with $500 million in funding, co-founded by former Amazon executive Rob Williams and neuroscientist Thomas Reardon. The company aims to build brain-inspired AI systems called Cortex AI that can run on 50 watts or less and continuously learn, addressing key limitations of current LLMs.

XOResNet introduces OR-ADD shortcut connections and XOR meta-residuals to address spike redundancy and information loss in deep spiking neural networks, achieving state-of-the-art results on Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet.

Introduces Eggroll, a low-rank evolution strategy for gradient-free training of spiking neural networks, reducing memory and time overhead while achieving competitive accuracy on N-MNIST.

A side project presents a Hebbian architecture AI model that avoids backpropagation and gradients, achieving 50 epochs on CIFAR-10 with emergent behaviors like accuracy dips followed by jumps and recovery after targeted damage.

This paper extends Equilibrium Propagation to skew-gradient systems and demonstrates an equivalence between deep Energy-Based Models and Hamiltonian neural networks, focusing on diffusively coupled Fitzhugh-Nagumo neurons. It derives a layer-wise Hamiltonian recurrence relation for inference in such networks.

This paper proposes a plug-and-play framework that implements spike-friendly approximations for Transformer nonlinearities (e.g., Softmax, SiLU, normalization) via population computation with LIF neurons and lightweight bit-shift scaling, achieving less than 1% accuracy drop on LLMs without fine-tuning.

This paper proposes a federated learning framework for spiking neural networks that addresses the challenge of heterogeneous temporal resolutions across edge devices, enabling collaborative training without sharing raw data while handling temporal mismatches.