1. A cognitive memory system configured for receiving input data or input patterns from one or more sensors, storing said input data or input patterns wherever storage space is available, and retrieving said input data or input patterns upon receipt of an input prompt or query pattern, wherein said cognitive memory system comprises: (a) a memory input line that carries said input data and input patterns throughout said cognitive memory system for recording wherever storage space is available; (b) a prompt line that carries said input prompt or query pattern throughout said cognitive memory system; (c) one or more memory segments, each memory segment having: (i) one or more memory folders, each memory folder configured for storing said input data or input patterns from said one or more sensors streaming in a sequence over time, and a scanner for continuously scanning the stored contents of said one or more folders and generating patterns; (ii) a trainable auto-associative neural network whose training input patterns are obtained from said scanner generated patterns and whose sensing input patterns are said input query patterns obtained from said prompt line, said trainable auto-associative neural network further including: a training algorithm for training said auto-associative neural network to reproduce said training input patterns at its output; (iii) a first comparator configured for pixel-by-pixel subtracting said sensing input patterns from the auto-associative neural network output to form first error patterns, (iv) first means for computing the magnitude or mean square of the first error patterns, (v) a first threshold device that closes a first switch when a sensing input pattern is identified as a hit query pattern when the magnitude or mean square of its first error pattern is below the first threshold level of said first threshold device, said first switch configured to connect a prompt memory buffer to said prompt line to store said hit query pattern in said prompt memory buffer; (vi) a second comparator configured for pixel-by-pixel subtraction of said scanner generated patterns received from said scanner from said hit query pattern stored in said prompt memory buffer and generating differences, the generated differences being second error patterns; (vii) second means for computing the magnitude or mean square of the second error patterns, (viii) a second threshold device that closes a second switch when there is a second hit when the magnitude or mean square of a second error pattern is below the second threshold level of said second threshold device, said second switch configured to connect the memory output line of said memory segment to deliver as output the contents of the memory folder containing the hit pattern associated with said second hit.
1. A cognitive memory system configured for receiving input data or input patterns from one or more sensors, storing said input data or input patterns wherever storage space is available, and retrieving said input data or input patterns upon receipt of an input prompt or query pattern, wherein said cognitive memory system comprises: (a) a memory input line that carries said input data and input patterns throughout said cognitive memory system for recording wherever storage space is available; (b) a prompt line that carries said input prompt or query pattern throughout said cognitive memory system; (c) one or more memory segments, each memory segment having: (i) one or more memory folders, each memory folder configured for storing said input data or input patterns from said one or more sensors streaming in a sequence over time, and a scanner for continuously scanning the stored contents of said one or more folders and generating patterns; (ii) a trainable auto-associative neural network whose training input patterns are obtained from said scanner generated patterns and whose sensing input patterns are said input query patterns obtained from said prompt line, said trainable auto-associative neural network further including: a training algorithm for training said auto-associative neural network to reproduce said training input patterns at its output; (iii) a first comparator configured for pixel-by-pixel subtracting said sensing input patterns from the auto-associative neural network output to form first error patterns, (iv) first means for computing the magnitude or mean square of the first error patterns, (v) a first threshold device that closes a first switch when a sensing input pattern is identified as a hit query pattern when the magnitude or mean square of its first error pattern is below the first threshold level of said first threshold device, said first switch configured to connect a prompt memory buffer to said prompt line to store said hit query pattern in said prompt memory buffer; (vi) a second comparator configured for pixel-by-pixel subtraction of said scanner generated patterns received from said scanner from said hit query pattern stored in said prompt memory buffer and generating differences, the generated differences being second error patterns; (vii) second means for computing the magnitude or mean square of the second error patterns, (viii) a second threshold device that closes a second switch when there is a second hit when the magnitude or mean square of a second error pattern is below the second threshold level of said second threshold device, said second switch configured to connect the memory output line of said memory segment to deliver as output the contents of the memory folder containing the hit pattern associated with said second hit. 9. The cognitive memory system of claim 1 , wherein: said autoassociative neural network is a multilayer perceptron trained by the back-propagation algorithm and implemented by means of parallel digital hardware; said input prompt or input query patterns are obtained from said contents of the memory folder containing said hit pattern associated with said second hit; said input data comprises visual or photographic images, said input data being first pre-processed by any one of or any combination of rotation, translation, scaling, varying brightness and contrast before carried throughout said cognitive memory system by said memory input line; visual or photographic input prompt or query patterns are first pre-processed by a combination of rotation, translation, scaling, varying brightness and contrast before being carried throughout said cognitive memory system by said prompt line; and said cognitive memory system is used for face recognition wherein: said input data or patterns are one or more facial images of known individual persons and their respective identities; said input query patterns are obtained by scanning images or photographs with a variable window, said variable window capable of variation by any combination of rotation, translation, scaling, brightness, and contrast; and said variable window outlines an identifiable face when there is said second hit, when a pattern or patterns appear on the memory output line with their identification.