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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 stimuli in some time window preceding each spike (here consisting of 3 time bins) are selected (color boxes) and then averaged to obtain the STA. The STA indicates that this neuron is selective for a bright spot of light just before the spike, in the top-left corner of the checkerboard.
The Galves–Löcherbach model (or GL model) is a mathematical model for a network of neurons with intrinsic stochasticity. [ 1 ] [ 2 ] In the most general definition, a GL network consists of a countable number of elements (idealized neurons ) that interact by sporadic nearly-instantaneous discrete events ( spikes or firings ).
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 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.
The firing neuron described above is called a spiking neuron. We will model the electrical circuit of the neuron in Section 3.6. There are two types of spiking neurons. If the stimulus remains above the threshold level and the output is a spike train, it is called the Integrate-and-Fire (IF) neuron model.
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The exponential integrate-and-fire model (EIF) is a biological neuron model, a simple modification of the classical leaky integrate-and-fire model describing how neurons produce action potentials. In the EIF, the threshold for spike initiation is replaced by a depolarizing non-linearity.