Abstract: A system and methods offering a dynamical model of cortical behavior is provided. In an illustrative implementation, the present invention offers a corticonic network comprising at least one parametrically coupled logistic map network (PCLMN)(205). The PCLMN offers a non-linear iterative map of cortical modules (or netlets) that when executed exhibit substantial cortical behaviors. The PCLMN accepts dynamic and/or static spatio-temporal input (210) and determines a fixed point attractor in state-space for that input. The PCLM (205) operates such that if the same or similar dynamic and/or static spatio-temporal input is offered over several iterations, the PCLMN converges to the same fixed point attractor is provided rendering adaptive learning. Further, the present invention contemplates the memorization or association of inputs using the corticonic network in a configuration where the PCLMN cooperates with another cortical module model (e.g. another PCLMN, associative memory module, etc.)(215).

Abstract: A system and methods offering a dynamical model of cortical behavior is provided. In an illustrative implementation, the present invention offers a corticonic network comprising at least one parametrically coupled logistic map network (PCLMN)(205). The PCLMN offers a non-linear iterative map of cortical modules (or netlets) that when executed exhibit substantial cortical behaviors. The PCLNM accepts dynamic and/or static spatio-temporal input (210) and determines a fixed point attractor in state-space for that input. The PCLM (205) operates such that if the same or similar dynamic and/or static spatio-temporal input is offered over several iterations, the PCLMN converges to the same fixed point attractor is provided rendering adaptive learning. Further, the present invention contemplates the memorization or association of inputs using the corticonic network in a configuration where the PCLNM cooperates with another cortical module model (e.g. another PCLMN, associative memory module, etc.)(215).

Abstract: A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.