Abstract: A Hamming neural network circuit which can be programmed and expanded is disclosed. The Hamming neural network includes an I/O circuit for inputting and outputting a plurality of standard patterns. A bi-directional transmission gate array is connected to the I/O circuit and controlled by a programming signal for transmitting the standard patterns. A plurality of standard pattern memory units is connected to the bi-directional transmission gate array for storing the plurality of standard patterns respectively. An address decoder is connected to the plurality of standard pattern memory units for addressing one of the plurality of standard pattern memory units. A plurality of pattern matching calculation circuit units are respectively connected to the plurality of standard pattern memory units for generating a plurality of matching rates between a to-be-recognized pattern and the plurality of standard patterns.

Abstract: The invention comprises e-circuits built from basic modules of e-cells which are capable of: recognizing a previously memorized percept anywhere within an arbitrarily large input field without incurring delay related to size of the search space; isolating a previously memorized percept within the input field when in the adjacent presence of other percepts and closely related distractors; locating and recognizing all occurrences of a repeated subfield within the input field, even in the presence of closely related distractors; and performing sequential shift attention between a number of different percepts in the input field. The invention is applicable to various recognition tasks including those in sensory domains such as speech and music recognition, vision, olefaction, and touch.

Abstract: A neural cellular network for implementing a so-called Chua's circuit, and comprising at least first, second and third cells having respective first and second input terminals and respective state terminals, the first and second input terminals being to receive a first and a second reference signal, respectively, and the first cell, and the second and third cells being of mutually different types.

Abstract: A digital artificial neural network (ANN) reduces memory requirements by storing sample transfer function representing output values for multiple nodes. Each nodes receives an input value representing the information to be processed by the network. Additionally, the node determines threshold values indicative of boundaries for application of the sample transfer function for the node. From the input value received, the node generates an intermediate value. Based on the threshold values and the intermediate value, the node determines an output value in accordance with the sample transfer function.

Abstract: A process is set forth in which cancer of the colon is assessed in a patient. The probabilities of developing cancer involves the initial step of extracting a set of sample body fluids from the patient. Fluids can be evaluated to determine certain marker constituents in the body fluids. Fluids which are extracted have some relationship to me development of cancer, precancer or tendency toward cancerous conditions. The body fluid markers are measured and other quantified. The marker data then is evaluated using a nonlinear technique exemplified through the use of a multiple input and multiple output neural network having a variable learning rate and training rate. The neural network is provided with data from other patients for the same or similar markers. Data from other patients who did and did not have cancer is used in the learning of the neural network which thereby processes the data and provides a determination that the patient has a cancerous condition, precancer cells or a tendency towards cancer.

Abstract: A nonrecurrent version of the Neural Network Binary Code Recognizer is disclosed. This Nonrecurrent Binary Code Recognizer, which decodes an input vector of n analog components into a decoded binary word of n bits, comprises an analog-to-digital converter, an inverter circuit, a digital summing circuit and a comparator circuit.