Search results
Results from the WOW.Com Content Network
A neural network model based on pulse generation time can be established. [17] Using the exact time of pulse occurrence, a neural network can employ more information and offer better computing properties. [18] The SNN approach produces a continuous output instead of the binary output of traditional artificial neural networks (ANNs). Pulse ...
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 ...
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.
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.
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.
Gerstner is the author of neuroscientific text books such as Spiking Neuron Models: Single neurons, populations, plasticity (Gerstner, W. and Kistler, W.M., 2002, Cambridge University Press) that introduced the field of spiking neural networks, [8] and Neuronal dynamics: From single neurons to networks and models of cognition (Gerstner, W ...
Simulation of neural oscillations at 10 Hz. Upper panel shows spiking of individual neurons (with each dot representing an individual action potential within the population of neurons), and the lower panel the local field potential reflecting their summed activity. Figure illustrates how synchronized patterns of action potentials may result in ...
A probabilistic neural network (PNN) is a four-layer feedforward neural network. The layers are Input, hidden pattern/summation, and output. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function.