AI News, BOOK REVIEW: Artificial Intelligence/Neural Networks/Natural Neural Networks

Artificial Intelligence/Neural Networks/Natural Neural Networks

The primary difference between a natural neural network and a Distributed Processing analog of a Neural Network, is the attempt to capture the function of a real neuron and real natural arrangements of neurons in the model.

From the Hebbian model used in early perceptrons to modern neural models that attempt to capture the biochemical threads that implement different forms of memory within the same cell, The idea has all along been to find a reasonable model for the neuron, and learn from implementations of that model knowledge of how natural neural systems might work.

It has been a long hard road, and while neural networks have gained and lost prominence in A.I., Neuro-scientists have been forced to go back to the Neural Network model, time and time again, as the most ethical approach to learning about natural networks of neurons.

Unlike other forms of Neuroscience, Neural models do not kill animals, in order to get their neurons, they do not torture animals in order to see how they will react, they do not even involve real animals, instead they torture recyclable electrons by making them flow through computer circuits.

To add to this rule however we must take into account the fact that new models of neural systems, incorporate learning threads that operate in parallel and implement short term, long term and perhaps even medium term memories.

In fact small networks of neurons connect to larger networks of neurons, forming a network throughout the whole body, with centers that process specific types of information in a number of centers of the body.

If we are going to deal with a system of the complexity of the brain we need new neural network models that can capture the variety in the structure of neurons, can explain the functions of Groups of different types of neurons, and explain why some similar neurons act as if they were a single solid group, where only one or two neurons fire for the whole group.

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