Dynamical brain model for use in data processing applications

A method to simulate cortical processing for use in processing data, comprising:

• segmenting a cortex into cortical columns, said cortical columns communicating with each other via short-range and longer-range communications paths;
• generating at least one parametrically coupled logistic map network (PCLMN) to model these communication paths, said PCLMN comprising one or more parametrically coupled logistic maps;
• using a difference equation in the PCLMN that represents neurons in the cortical columns in the modeling of the cortex;
• providing at least one dynamic and/or static input to said at least one PCLMN;
• and
• processing said at least one dynamic and/or static input by said PCLMN to determine a fixed-point attractor for said at least one dynamic and/or static input in state-space.

A system to model cortical processing for use in processing data, comprising:

• a memory;
• at least one parametrically coupled logistic map network (PCLMN) stored in the memory, said PCLMN providing a dynamic and/or static non-linear representation of cortical connections as observed in a biological cortex using a difference equation that represents neurons in cortical columns,
• wherein said at least one PCLMN accepts at least one dynamic and/or static input and processes said dynamic and/or static input to identify a fixed-point attractor for said at least one dynamic and/or static input in state space,
• wherein said at least one PCLMN operates to adapt providing the same output to similar or same inputs thereby engaging in learning;
• and
• wherein said at least one dynamic and/or static input comprises at least one spatio-temporal stimulus.

A system to model cortical processing for use in processing data, comprising:

• a memory;
• at least one parametrically coupled logistic map network (PCLMN) stored in the memory, said PCLMN providing a dynamic and/or static non-linear representation of cortical connections as observed in a biological cortex using a difference equation that represents neurons in cortical columns,
• wherein said at least one PCLMN accepts at least one dynamic and/or static input and processes said dynamic and/or static input to identify a fixed-point attractor for said at least one dynamic and/or static input in state space,
• wherein said at least one PCLMN operates to adapt providing the same output to similar or same inputs thereby engaging in learning;
• and
• wherein said adaptation of said at least one PCLMN occurs through few iterations of inputs.

A system to model cortical processing for use in processing data, comprising:

• a memory;
• at least one parametrically coupled logistic map network (PCLMN) stored in the memory, said PCLMN providing a dynamic and/or static non-linear representation of cortical connections as observed in a biological cortex using a difference equation that represents neurons in cortical columns,
• wherein said at least one PCLMN accepts at least one dynamic and/or static input and processes said dynamic and/or static input to identify a fixed-point attractor for said at least one dynamic and/or static input in state space,
• wherein said at least one PCLMN operates to adapt providing the same output to similar or same inputs thereby engaging in learning;
• and
• at least one additional cortical model module, said at least one additional cortical model module accepting the output of said at least one PCLMN as input and is capable of classifying in real time said output of said at least one PCLMN as being novel simulating memorization and/or association cortical functions.

A method to model the cortex and use said model in order to process input data, comprising:

• providing a corticonic network, said corticonic network comprising at least one parametrically coupled logistic map network (PCLMN) operating on at least one dynamic and/or static input, said at least one dynamic and/or static input comprising spatio-temporal stimulus and said PCLMN modeling cortical functions using a difference equation;
• processing said at least one dynamic and/or static input using said corticonic network;
• providing a fixed point attractor in state space for said at least one dynamic and/or static input;
• and
• performing memorization and/or association on said outputs of said corticonic network, said corticonic network engaging in memorization through the use of at least one additional PCLMN tandemly coupled to said at least one PCLMN and operating to classify novel output data from said at least one PCLMN in order to process the input data.

A system modeling the cortex for use in processing data comprising:

• a memory;
• a corticonic network,
• wherein said corticonic network comprises a first parametrically coupled logistic map network (PCLMN) stored in said memory, said first PCLMN accepting dynamic and/or static inputs and determining a fixed point attractor in state-space to describe at least one dynamic and/or static input, said first PCLMN models close-range intercortical connections by space-time filtering of the inputs in order to reduce redundancies in patterns of the inputs using an autonomous adaptation algorithm, and
• wherein said corticonic network comprises a second PCLMN, said second PCLMN tandemly coupled to said first PCLMN to accept the output of said first PCLMN as input and operates to identify non-novel output of said first PCLMN, said identification of non-novel output representative of memorization and/or association cortical functions, and
• wherein a feedback ioop operates between the output of said second PCLMN and the input of said first PCLMN.

A method of applying a dynamical brain model for processing data comprising:

• identifying a data processing problem;
• modeling the data processing problem in a corticonic network;
• configuring said corticonic network to the parameters of said data processing problem;
• executing said corticonic network using a computer, the corticonic network modeled by a parametrically coupled logistic map (PCLM) that models cortical functions using a difference equation;
• using the corticonic network for processing the data and providing a processed output;
• obtaining results from said execution of said corticonic network;
• and
• verifying said results of said execution of said corticonic network by comparing said results of the processed output with a comparable real-world model.