Method and apparatus for neural networking using semantic attractor architecture

A neural network system for processing information in an unsupervised manner, comprising:

• a feedforward architecture having a plurality of channel means, each comprising a plurality of layers of nodes with random connections from one layer to the next, for transforming input arrays from prior layers or the environment into output arrays having fractal dimension for subsequent layers or output devices, said plurality of channels further comprising:
• at least one input layer means devoted to processing input information;
• a plurality of processing layer means;
• at least one feedback connection from one of said processing layer means to a prior one of said processing layer means;
• at least one output layer means, in an output channel means of said plurality of channels, devoted to processing outputs;
• means for providing feedback from said at least one output channel means back to said at least one processing layer means;
• inputs from parallel channels to said at least one processing layer means;
• and,
• said channel means comprising means for applying selective excitation and inhibition to nodes and means for applying learning rules which are based upon non-stationary statistical processes to inputs of said channel means to create constellations of activations having fractal dimension.

A neural network system for processing information in an unsupervised manner, comprising:

• a feedforward architecture having a plurality of self-adjusting variable-width multiple-layer channel means, each comprising a multiple-layered set of nodes, for transforming input arrays from prior layers or the environment into output arrays of fractal dimension for subsequent layers or output devices, with random connections from one layer to the next;
• at least one input layer means devoted to processing input information;
• a plurality of processing layer means;
• feedback connections from at least one of said processing layer means back to a prior processing layer means;
• at least one output layer means, within an output channel means, devoted to processing outputs;
• means for providing feedback from said at least one output channel means back to at least one of said plurality of processing layer means;
• and,
• inputs from parallel channels to said at least one processing layer means.

A process for using a first neural network to program a second neural network, comprising:

• using a plurality of parallel multiple-layer channels, each comprising a multiple-layered set of nodes, to transform input arrays from prior layers or the environment into output arrays of fractal dimension;
• using at least one input layer to process inputs;
• using outputs from at least one of said plurality of parallel multiple-layer channels as inputs to at least one processing layer having lateral connections;
• using at least one output layer to process outputs;
• using feedback connections to connect said at least one output layer and said at least one processing layer;
• assigning connection weights to said feedback connections and said lateral connections;
• providing target data to said input layers;
• adjusting said connection weights;
• varying said feedback connections and said lateral connections;
• applying selective excitation and inhibition to nodes of said channels and using said nodes to apply learning rules which are based upon non-stationary statistical processes to inputs of said channels to create constellations of activations having fractal dimension;
• recording at least one successful combination of connection weights, feedback connections, and lateral connections, which together comprise a set having fractal dimension;
• and,
• using said at least one successful combination to program a second neural network.

A system for using a first neural network to program a second neural network, comprising:

• a first neural network, further comprising:
• a plurality of multiple-layer channels, each comprising a multiple-layered set of nodes, for transforming input arrays from prior layers or the environment to output arrays of fractal dimension for subsequent layers or output devices;
• at least one input layer;
• at least one processing layer having lateral connections;
• at least one output layer in at least one output channel;
• feedback connections between said at least one output channel and said at least one processing layer;
• means for assigning connection weights to said feedback connections and said lateral connections;
• means for providing target data to said input layers;
• means for evolving said connection weights;
• means for varying said feedback connections and said lateral connections;
• means for applying selective excitation and inhibition to nodes of said channels and using said nodes to apply learning rules which are based upon non-stationary statistical processes to inputs of said channels to create constellations of activations having fractal dimension;
• means for recording at least one set of fractal dimension, said set comprising a successful combination of connection weights, feedback connections, and lateral connections;
• and,
• means for using said at least one successful combination to program a second neural network.

A method for processing information, comprising:

• using a feedforward neural network system to process said information in an unsupervised manner, further comprising the steps of:
• providing a plurality of multiple-layer channels, each comprising a multiple-layered set of nodes, for transforming input arrays from prior layers or the environment to output arrays of fractal dimension for subsequent layers or output devices;
• using at least one layer to process input information;
• using at least one processing layer to process outputs of said input layer;
• using at least one output layer in at least one output channel to process outputs;
• feeding back data from said at least one output channel back to said at least one processing layer;
• using outputs from parallel channels as inputs to said at least one processing layer;
• and,
• applying selective excitation and inhibition to nodes of said channels and using said nodes to apply learning rules which are based upon non-stationary statistical processes to inputs of said channel means to create constellations of activations with fractal dimensions.