Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes...

these systems encode information in the networks using spikes, mimicking the functionality of spiking neural networks in optical and photonic hardware. Photonic...

deployment of spiking neural networks (SNN), and the AKD1000 neuromorphic processor, a hardware implementation of their spiking neural network system. BrainChip's...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

train spiking neurons for precise spike sequence generation in response to specific input patterns. In a paper that received the 2016 Neural Networks Best...

characteristic spiking behavior of action potential generating neurons. Non-spiking neural networks are integrated with spiking neural networks to have a synergistic...

neural network, another term for an artificial neural network Spiking neural network, a type of artificial neural network Spatial neural network, another...

In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the...

SpiNNaker (spiking neural network architecture) is a massively parallel, manycore supercomputer architecture designed by the Advanced Processor Technologies...

A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). Biological...

A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce...

Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series...

proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and...

multiplicative units. Neural networks using multiplicative units were later called sigma-pi networks or higher-order networks. LSTM became the standard...

or as intrinsic oscillators. Bursting is another form of rhythmic spiking. Spiking patterns are considered fundamental for information coding in the brain...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

with strong acceleration via graphics processing units (GPU) Deep neural networks built on a tape-based automatic differentiation system In 2001, Torch...

NEST is a simulation software for spiking neural network models, including large-scale neuronal networks. NEST was initially developed by Markus Diesmann...

artificial spiking neural networks. Using this approach the weight of a connection between two neurons is increased if the time at which a presynaptic spike (...

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine...

translation service to neural machine translation (NMT), replacing statistical phrase-based models with deep recurrent neural networks. These early NMT systems...

developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's...

The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It was...

task space and facilitate problem solving. Siamese neural network is composed of two twin networks whose output is jointly trained. There is a function...

Lapique in 1907). Non-spiking cells, spiking cells, and their measurement Not all the cells of the nervous system produce the type of spike that defines the...

roots in the early study of neural networks such as Jeffrey Elman's 1993 paper Learning and development in neural networks: the importance of starting...

words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical...

state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

scaling can also be applied to deep neural network classifiers. For image classification, such as CIFAR-100, small networks like LeNet-5 have good calibration...