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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.
The left graph shows a green function G that is phase-shifted relative to function F by a time displacement of 𝜏. The middle graph shows the function F and the phase-shifted G represented together as a Lissajous curve. Integrating F multiplied by the phase-shifted G produces the right graph, the cross-correlation across all values of 𝜏.
The summation in Riemann's formula is not absolutely convergent, but may be evaluated by taking the zeros ρ in order of the absolute value of their imaginary part. The function li occurring in the first term is the (unoffset) logarithmic integral function given by the Cauchy principal value of the divergent integral
The temporal mean is the arithmetic mean of a series of values over a time period. Assuming equidistant measuring or sampling times, it can be computed as the sum of the values over a period divided by the number of values.
The figure on the right was created using A = 1, x 0 = 0, y 0 = 0, σ x = σ y = 1. The volume under the Gaussian function is given by V = ∫ − ∞ ∞ ∫ − ∞ ∞ f ( x , y ) d x d y = 2 π A σ X σ Y . {\displaystyle V=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }f(x,y)\,dx\,dy=2\pi A\sigma _{X}\sigma _{Y}.}
This function has an asymptotic expansion as z tends to 0 that is given by the original divergent series. This is a typical example of the fact that Borel summation will sometimes "correctly" sum divergent asymptotic expansions.
That is, the goal is to minimize the maximum value of () (), where P(x) is the approximating polynomial, f(x) is the actual function, and x varies over the chosen interval.