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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.
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]
This is an example of a distractor, which is a situational cue that created a negative outcome when a relevant trait is activated. [4] In this example, the organizational cues of whether a high sociability environment is expected between coworkers would influence the strength of the cue and the level of activation.
Google has used Chisel to develop a Tensor Processing Unit for edge computing. [7] Some developers prefer Chisel as it requires 5 times lesser code and is much faster to develop than Verilog. [8] Circuits described in Chisel can be converted to a description in Verilog for synthesis and simulation using a program named FIRRTL. [9] [better ...
Interactive activation and competition (IAC) networks are artificial neural networks used to model memory and intuitive generalizations. They are made up of nodes or artificial neurons which are arrayed and activated in ways that emulate the behaviors of human memory.
Example - Watching a movie within the genre of thriller, suspense or horror. Explanation - The initial stimulus would be the act or situation of watching a movie within one of these genres. Physical responses including an increased heart rate and elevated levels of adrenaline would be the physiological arousal components of the theory.
It is composed of three different values: A – activation, I – input-output gating, and M – modulation. The model is limited however, in that it cannot yet explain the regional differences in brain activity that distinguish REM sleep from waking. Other limitations include the inability to quantifiably identify and measure M in humans.
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