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Neurogrid is a board that can simulate spiking neural networks directly in hardware. (Stanford University) SpiNNaker (Spiking Neural Network Architecture) uses ARM processors as the building blocks of a massively parallel computing platform based on a six-layer thalamocortical model.
The principles of STDP can be utilized in the training of 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 ( t p r e {\displaystyle t_{pre}} ) occurs is shortly before the time of a post synaptic spike( t p o s t {\displaystyle t_{post ...
The spike response model (SRM) [1] is a spiking neuron model in which spikes are generated by either a deterministic [2] or a stochastic [1] threshold process. In the SRM, the membrane voltage V is described as a linear sum of the postsynaptic potentials (PSPs) caused by spike arrivals to which the effects of refractoriness and adaptation are added.
Biological neuron models, also known as spiking neuron models, [1] are mathematical descriptions of the conduction of electrical signals in neurons. Neurons (or nerve cells) are electrically excitable cells within the nervous system, able to fire electric signals, called action potentials, across a neural network.
A liquid 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 neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other.
Winner-take-all is a general computational primitive that can be implemented using different types of neural network models, including both continuous-time and spiking networks. [1] [2] Winner-take-all networks are commonly used in computational models of the brain, particularly for distributed decision-making or action selection in the cortex.
NEST is a simulation software for spiking neural network models, including large-scale neuronal networks. NEST was initially developed by Markus Diesmann and Marc-Oliver Gewaltig and is now developed and maintained by the NEST Initiative.
Spiking neural networks with axonal conduction delays exhibit polychronization, and hence could have a very large memory capacity. [102] SNN and the temporal correlations of neural assemblies in such networks—have been used to model figure/ground separation and region linking in the visual system.