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Neural Memory

Traditional computers store memory in arrays of binary dots -- each of which is in either an "on" or "off" position. Destruction of any portion of this array results in irreversible memory loss. Neural network computers store memory as: the relative strengths of connections among, and summation parameters within, multiple artificial neurons. Neural memory is more adaptive, associative, and robust than traditional binary memory. The human brain is made of up hundreds of billions of interconnected cells - the many key of which are neurons and glial tissue. Neurons receive, process, and relay electrochemical impulses. The grey substance of the brain is made up of the neuron cell bodies. Glial tissue give material and electrochemical support for neurons. Together, these cells make up an organic neural network with approximately a trillion dynamic neural connections.

The storage of memories in the brain is done in a distributed, parallel, and interconnected fashion. PET scans, which indicate cellular activity, show activity is numerous diverse areas of the brain during memory recall. This differs from the saving of memory in a discrete place in many calculators. This makes the brain, and its memories, more resilient to damage than calculators. In the event of injury or malady, the whole configuration of the brain need not be restored in its entirety to restore memories and functioning.

Biomedical cybernetics an adapting area that focuses on assembling mechanical devices that interface with biologic organisms, and vice versa. Humans are already being implanted with electromechanical machines such as pacemakers and cochlear implants. With a little imagination, one could even view alternative types of human organizations as intelligent cybernetic entities in which humans are pieces.

Neural networks are systems of programs and data that emulate the functioning of organic neurons using gradient-based practice, fuzzy logic, genetic algorithms, and Bayesian procedures. They are presently being used to simulation signal processing and time-series investigation, predict the weather, and even experimental design organic thinking. Neural networks are primed with massive quantities of data and rules regarding information associations that enable the networks to evolve into deeper knowledge strata.

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