Neural networks : the official journal of the International Neural Network Society
Sep 27, 2019
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...
Advanced materials (Deerfield Beach, Fla.)
Sep 26, 2019
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behavior...
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neur...
Neural networks : the official journal of the International Neural Network Society
Aug 26, 2019
In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plast...
Neural networks : the official journal of the International Neural Network Society
Aug 19, 2019
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical p...
IEEE transactions on biomedical circuits and systems
Jul 15, 2019
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-...
Neural networks : the official journal of the International Neural Network Society
Jul 9, 2019
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of S...
The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs)....
IEEE transactions on neural networks and learning systems
Jun 18, 2019
Spiking neural networks (SNNs) are of interest for applications for which conventional computing suffers from the nearly insurmountable memory-processor bottleneck. This paper presents a stochastic SNN architecture that is based on specialized logic-...
Neural networks : the official journal of the International Neural Network Society
Jun 18, 2019
The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for...