Search results
Results from the WOW.Com Content Network
While the notion of an artificial spiking neural network became very popular only during the first quarter of the twenty-first century, [8] [9] [10] there are a number of studies between 1980 and 1995 that supported the concept and in which the first models of this type of artificial neural networks appeared to simulate non-algorithmic ...
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.
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.
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.
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). [1] CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state.
The associative neural network (ASNN) is an extension of committee of machines that combines multiple feedforward neural networks and the k-nearest neighbor technique. It uses the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN.
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.
Brian is aimed at researchers developing models based on networks of spiking neurons. The general design is aimed at maximising flexibility, simplicity and users' development time. [ 2 ] Users specify neuron models by giving their differential equations in standard mathematical form as strings , create groups of neurons and connect them via ...