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Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. [1] The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes.
The activation-synthesis hypothesis, proposed by Harvard University psychiatrists John Allan Hobson and Robert McCarley, is a neurobiological theory of dreams first published in the American Journal of Psychiatry in December 1977.
Definition of activation: Activation can be defined in a variety of ways. For example, in a Boltzmann machine, the activation is interpreted as the probability of generating an action potential spike, and is determined via a logistic function on the sum of the inputs to a unit.
The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .
In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. The perceptron algorithm is also termed the single-layer perceptron , to distinguish it from a multilayer perceptron , which is a misnomer for a more complicated neural network.
The default mode network is most commonly defined with resting state data by putting a seed in the posterior cingulate cortex and examining which other brain areas most correlate with this area. [21] The DMN can also be defined by the areas deactivated during external directed tasks compared to rest. [19]
Winner-take-all is a computational principle applied in computational models of neural networks by which neurons compete with each other for activation. In the classical form, only the neuron with the highest activation stays active while all other neurons shut down; however, other variations allow more than one neuron to be active, for example the soft winner take-all, by which a power ...
In a feedback or parallel constraint satisfaction network, activation passes around symmetrically connected nodes until the activation of all the nodes asymptotes or "relaxes" into a state that satisfies the constraints among the nodes. This process allows for the integration of a number of different sources of information in parallel. [2]