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The biologically inspired Hodgkin–Huxley model of a spiking neuron was proposed in 1952. This model describes how action potentials are initiated and propagated. . Communication between neurons, which requires the exchange of chemical neurotransmitters in the synaptic gap, is described in various models, such as the integrate-and-fire model, FitzHugh–Nagumo model (1961–1962), and ...
The spiking neuron model by Nossenson & Messer [72] [73] [74] produces the probability of the neuron firing a spike as a function of either an external or pharmacological stimulus. [72] [73] [74] The model consists of a cascade of a receptor layer model and a spiking neuron model, as shown in Fig 4. The connection between the external stimulus ...
The focus of this article is a comprehensive view of modeling a neural network (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels.
The following example simulates spiking activity in a sparse random network with recurrent excitation and inhibition [1] The figure shows the spiking activity of 50 neurons as a raster plot. Time increases along the horizontal axis, neuron id increases along the vertical axis. Each dot corresponds to a spike of
3D Vizualization of the Galves–Löcherbach model simulating the spiking of 4000 neurons (4 layers with one population of inhibitory neurons and one population of excitatory neurons each) in 180 time intervals. The Galves–Löcherbach model (or GL model) is a mathematical model for a network of neurons with intrinsic stochasticity. [1] [2]
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 ...
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.
In the CoDi model the neurons sum the incoming signal values and fire after a threshold is reached. This behavior of the neuron bodies can be modified easily to suit a given problem. The output of the neuron bodies is passed on to its surrounding axon cells. Axonal cells distribute data originating from the neuron body.