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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.
Therefore, in order to achieve threshold and generate an action potential, the postsynaptic neuron has the capacity to add up all of the incoming EPSPs based on the mechanism of summation, which can occur in time and space. Temporal summation occurs when a particular synapse is stimulated at a high frequency, which causes the postsynaptic ...
Hence, the function of coincidence detection is to reduce the jitter caused by spontaneous neuronal activity, and while random sub-threshold stimulations from cells may not often fire coincidentally, coincident synaptic inputs derived from a unitary external stimulus ensure that a target neuron will fire as a result of the stimulus.
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 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}.}
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.
Sound pressure is the difference, in a given medium, between average local pressure and the pressure in the sound wave. A square of this difference (i.e., a square of the deviation from the equilibrium pressure) is usually averaged over time and/or space, and a square root of this average provides a root mean square (RMS) value.