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Very large interconnected networks are called large scale brain networks, and many of these together form brains and nervous systems. Signals generated by neural networks in the brain eventually travel through the nervous system and across neuromuscular junctions to muscle cells, where they cause contraction and thereby motion. [2]
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
Neural networks (i.e., artificial neural networks (ANNs) or simulated neural networks (SNNs)), are a subset of machine learning and are widely used as deep learning algorithms. Gleaned from the terminology itself, the name and structure of the models are inspired by the mechanism of human brain, which simulates the way that neurons signal to ...
It is hypothesized that the elementary biological unit is an active cell, called neuron, and the human machine is run by a vast network that connects these neurons, called neural (or neuronal) network. [5] The neural network is integrated with the human organs to form the human machine comprising the nervous system. [citation needed]
The term 'computational neuroscience' was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California, at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks.
A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to studying the neuroscience of intelligence consisted of correlating external head parameters, for example head circumference, to intelligence. [1] Post-mortem measures of brain weight and brain volume have also been used. [1]
The text by Rumelhart and McClelland [9] (1986) provided a full exposition on the use of connectionism in computers to simulate neural processes. Artificial neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and ...
Although direct human brain emulation using artificial neural networks on a high-performance computing engine is a commonly discussed approach, [4] there are other approaches. An alternative artificial brain implementation could be based on Holographic Neural Technology (HNeT) non linear phase coherence/decoherence principles.