Search results
Results from the WOW.Com Content Network
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.
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 size of the neuron can also affect the inhibitory postsynaptic potential. Simple temporal summation of postsynaptic potentials occurs in smaller neurons, whereas in larger neurons larger numbers of synapses and ionotropic receptors as well as a longer distance from the synapse to the soma enables the prolongation of interactions between neurons.
Temporal summation occurs when a particular synapse is stimulated at a high frequency, which causes the postsynaptic neuron to sum the incoming EPSPs and thus increases the chance of the neuron firing an action potential.
These two estimations of time do not involve the same neuroanatomical areas. For example, implicit timing often occurs to achieve a motor task, involving the cerebellum, left parietal cortex, and left premotor cortex. Explicit timing often involves the supplementary motor area and the right prefrontal cortex. [10]
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.
Ramanujan summation is a technique invented by the mathematician Srinivasa Ramanujan for assigning a value to divergent infinite series.Although the Ramanujan summation of a divergent series is not a sum in the traditional sense, it has properties that make it mathematically useful in the study of divergent infinite series, for which conventional summation is undefined.