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A diagram of temporal summation. At any given moment, a neuron may receive postsynaptic potentials from thousands of other neurons. Whether threshold is reached, and an action potential generated, depends upon the spatial (i.e. from multiple neurons) and temporal (from a single neuron) summation of all inputs at that moment.
The two ways that synaptic potentials can add up to potentially form an action potential are spatial summation and temporal summation. [5] Spatial summation refers to several excitatory stimuli from different synapses converging on the same postsynaptic neuron at the same time to reach the threshold needed to reach an action potential.
Fig. 1: Spatial and temporal summation. Two EPSPs innervated in rapid succession sum to produce a larger EPSP, or an action potential in the postsynaptic cell. Coincidence detection relies on separate inputs converging on a common target.
The same proof can be interpreted as summing the entries of the incidence matrix of the graph in two ways, by rows to get the sum of degrees and by columns to get twice the number of edges. [5] For graphs, the handshaking lemma follows as a corollary of the degree sum formula. [8]
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.
A Fourier transform and 3 variations caused by periodic sampling (at interval T) and/or periodic summation (at interval P) of the underlying time-domain function.. In mathematics, any integrable function can be made into a periodic function with period P by summing the translations of the function () by integer multiples of P.
In mathematics, a summation equation or discrete integral equation is an equation in which an unknown function appears under a summation sign. The theories of summation equations and integral equations can be unified as integral equations on time scales [ 1 ] using time scale calculus .
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.