Results 91 to 100 of about 60,411 (286)
SNNAX - Spiking Neural Networks in JAX
2024 International Conference on Neuromorphic Systems (ICONS)Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN.
Lohoff, Jamie +2 more
openaire +3 more sources
Propagation of Spike Sequences in Neural Networks [PDF]
SIAM Journal on Applied Dynamical Systems, 2010 Precise spatiotemporal sequences of action potentials are observed in many brain areas and are thought to be involved in the neural processing of sensory stimuli. Here, we examine the ability of spiking neural networks to propagate stably a spatiotemporal sequence of spikes in the limit where each neuron fires only one spike.
openaire +3 more sources
Advanced Functional Materials, EarlyView.Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone +8 more
wiley +1 more source
Spiking Optical Patterns and Synchronization
, 2007 We analyze the time resolved spike statistics of a solitary and two mutually interacting chaotic semiconductor lasers whose chaos is characterized by apparently random, short intensity spikes.
Aviad, Yaara +7 more
core +1 more source
Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks
, 2022 Accepted by ...
Yufei Guo +7 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.A fully programmable, dual‐inductive switchable halide perovskite memristor is demonstrated through precise BDAI2‐mediated interface engineering. This ion‐modulating layer suppresses stochastic filamentary growth, enabling stable, non‐filamentary switching via dynamic barrier modulation.
So‐Yeon Kim, Juan Bisquert
wiley +1 more source
Advanced Healthcare Materials, EarlyView.Implantable optoelectrical devices are an effective resource for the modulation and monitoring of neural activity with high spatiotemporal resolution. This review discusses current challenges faced by these devices and outlines future perspectives for the development of next‐generation neural interfaces targeting chronic, multisite, and multimodal ...
Stella Aslanoglou +4 more
wiley +1 more source
Wide learning: Using an ensemble of biologically-plausible spiking neural networks for unsupervised parallel classification of spatio-temporal patterns [PDF]
, 2018 Spiking neural networks have been previously used to perform tasks such as object recognition without supervision. One of the concerns relating to the spiking neural networks is their speed of operation and the number of iterations necessary to train and
Bentley, P, Kozdon, K
core
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
, 2019 The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Choe, Hyeokjun +3 more
core +1 more source
Advanced Healthcare Materials, EarlyView.PolyGraph, a flexible graphene‐polycaprolactone nanocomposite, unites conductivity, biocompatibility, and processability for next‐generation neural interfaces. Fabricated into microneedle arrays with ultra‐flexible backings, PolyGraph enables bidirectional neuronal recording and stimulation in brain tissue, advancing brain‐computer interface (BCI) and ...
Jack Maughan +12 more
wiley +1 more source