Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small ...
人工神经网络（artificial neural network，缩写ANN），简称神经网络（neural network，缩写NN），是一种模...
It works as a transistor, with one of the terminals controlling the flow of electricity between the other two. Like a neural path in a brain being reinforced through learning, the researchers program it by discharging and recharging it repeatedly. Through this training, they have been able to ...
IBM 在诸如 IEDM 之类的会议上非常活跃，而且这似乎有一个很好的光管地方，因为他们在这里公布的研究成果...
In this paper we report application of biologically based dynamic synapse neural network (DSNN) on perimeter protection. More specifically, the purpose is ... AA Dibazar,HO Park,TW Berger - International Joint Conference on Neural Networks 被引量: 32发表: 2007年 Dynamic Adaptive Neural Network ...
python neural-network mnist-classification spiking-neural-networks spike-time-dependent-plasticity neuromorphic synapse spike-trains neuromorphic-hardware Updated Jul 29, 2022 Python wso2 / product-apim Star 863 Code Issues Pull requests Welcome to the WSO2 API Manager source code! For info ...
A stochastic synapse for use in a neural network, comprising: first and second magnetic tunnel junction (MTJ) devices, each MTJ device having a fixed layer port and a free layer port; a first and second control circuit, each connected respectively to the free layer port of the first and se...
Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible ...
New techniques can image the activity of many inputs, and shed light on how single neurons perform computations in response.rnSynaptic communication between neurons is fundamental to how the brain processes and transforms information. Uncovering the neural circuitry has therefore been a major endeavour...
Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike...