Search results
Results from the WOW.Com Content Network
Typical level-of-processing theory would predict that picture encodings would create deeper processing than lexical encoding. "Memory over the short term and the long term has been thought to differ in many ways in terms of capacity, the underlying neural substrates, and the types of processes that support performance." [13]
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]
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 .
Process theories are important in management and software engineering. [3] Process theories are used to explain how decisions are made [4] how software is designed [5] [6] and how software processes are improved. [7] Motivation theories can be classified broadly into two different perspectives: Content and Process theories.
A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. [1] Multiple neural circuits interconnect with one another to form large scale brain networks. [2] Neural circuits have inspired the design of artificial neural networks, though there are significant differences.
There are theories proposed of quantum consciousness based on quantum mechanics. [7] There is an apparent redundancy and parallelism in neural networks so, while activity in one group of neurons may correlate with a percept in one case, a different population may mediate a related percept if the former population is lost or inactivated.
The model, in modern language, is an artificial neural network with ReLU activation function. [9] In a series of papers, Householder calculated the stable states of very simple networks: a chain, a circle, and a bouquet. Walter Pitts' first two papers formulated a mathematical theory of learning and conditioning.
This definition is not the same as that defined in functional connectivity. The modular approach is intended to build cognitive models and is, in complexity, between the anatomically defined brain regions (defined as macrolevel in brain connectivity) and the computational model at the neuron level. There are three views of modules for modeling.