Corticonic models of brain mechanisms underlying cognition and intelligence

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it:(a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime bymeans of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo–cortical loop, (e) distinguishes between redundant (structured)and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo–cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions. Physics of Life Reviews 4 (2007) 223–252 © 2007 Elsevier B.V. All rights reserved

Optical implementation of the Hopfield model

Optical implementation of content addressable associative memory based on the Hopfield model for neural networks and on the addition of nonlinear iterative feedback to a vector-matrix multiplier is described. Numerical and experimental results presented show that the approach is capable of introducing accuracy and robustness to optical processing while maintaining the traditional advantages of optics, namely, parallelism and massive interconnection capability. Moreover a potentially useful link between neural processing and optics that can be of interest in pattern recognition and machine vision is established

Dynamics of electron-trapping materials under blue light and near infrared exposure: an improved model

Dynamics of electron-trapping materials (ETMs) is investigated. Based on experimental observations, evolution of the ETM\u27s luminescence is mathematically modeled by a nonlinear differential equation. This improved model can predict dynamics of ETM under blue light and near-infrared (NIR) exposures during charging, discharging, simultaneous illumination, and in the equilibrium state. The equilibrium-state luminescence of ETM is used to realize a highly nonlinear optical device with potential applications in nonlinear optical signal processing

Optical Realization of the Retinal Ganglion Receptive Fields in Electron-Trapping Material Thin Film

Optical control of the electron-trapping material is used to model the retinal ganglion cell’s receptive field. Using this approach all the retinal image processing can be done on the surface of a thin film of this material

Realization of Receptive Fields with Excitatory and Inhibitory Responses on Equilibrium-State Luminescence of Electron Trapping Material Thin Film

Our theoretical modelings and experimental observations illustrate that the equilibrium-state luminescence of electron-trapping materials (ETMs) can be controlled to produce either excitatory or inhibitory responses to the same optical stimulus. Because of this property, ETMs have a unique potential in optical realization of neurobiologically based parallel computations. As a classic example, we have controlled the equilibrium-state luminescence of a thin film of this stimulable storage phosphor to make it behave similarly to the receptive fields of sensory neurons in the mammalian visual system, which are responsible for early visual processing

Self-Organization in a Parametrically Coupled Logistic Map Network: A Model for Information Processing in the Visual Cortex

In this paper, a new model seeking to emulate the way the visual cortex processes information and interacts with subcortical areas to produce higher level brain functions is described. We developed a macroscopic approach that incorporates salient attributes of the cortex based on combining tools of nonlinear dynamics, information theory, and the known organizational and anatomical features of cortex. Justifications for this approach and demonstration of its effectiveness are presented. We also demonstrate certain capabilities of this model in producing efficient sparse representations and providing the cortical computational maps

Optical Realization of Bio-inspired Spiking Neurons In Electron Trapping Material Thin

A thin film of electron-trapping material (ETM), when combined with suitable optical bistability, is considered as medium for optical implementation of bio-inspired neural nets. The optical mechanism of ETM under blue light and NIR exposure has the inherent ability at the material level to mimic the crucial components of the stylized Hodgkin-Huxley model of biological neuron. Combining this unique property with high resolution capability of ETM, a dense network of bio-inspired neurons can be realized in a thin film of this infrared stimulable storage phosphore. The work presented here, when combined with suitable optical bistability and optical interconnectivity, has the potential of producing an artificial nonlinear excitable medium analogue to cortical tissue

An analytic model for the dynamics of electron trapping materials with applications in nonlinear optical signal processing

In this paper the optical mechanism and dynamics of electron trapping material under simultaneous illumination with two wavelengths is investigated. Our analytical model proves that the equilibrium state luminescence of such a material can be controlled to produce highly nonlinear behavior with potential applications in nonlinear optical signal processing and optical realization of nonlinear dynamical systems. Combining this new approach with state-of-the-art fast spatial light modulators and CCD cameras that can precisely control and measure exposure, large arrays of nonlinear processing elements can be accommodated in a thin film of this material

A CMOS Monolithic Implementation of a Nonliniear Interconnection Module for a Corticonic Network

A nonlinear interconnection module for a corticonic network is designed and fabricated in a 0.6µm CMOS process. The module uses NMOS transistors in weak-inversion for nonlinearity. A calibration scheme is developed to compensate for the process and temperature variations of the circuit. The designed module has an area of 0.35 sq. mm2. It consumes 200mW of power, with 5V power supply. Simulation results show that the circuit is able to implement the target parametric coupling function accurately

Analog Realization of Arbitrary One-Dimensional Maps

An increasing number of applications of a one-dimensional (1-D) map as an information processing element are found in the literature on artificial neural networks, image processing systems, and secure communication systems. In search of an efficient hardware implementation of a 1-D map, we discovered that the bifurcating neuron (BN), which was introduced elsewhere as a mathematical model of a biological neuron under the influence of an external sinusoidal signal, could provide a compact solution. The original work on the BN indicated that its firing time sequence, when it was subject to a sinusoidal driving signal, was related to the sine-circle map, suggesting that the BN can compute the sine-circle map. Despite its rich array of dynamical properties, the mathematical description of the BN is simple enough to lend itself to a compact circuit implementation. In this paper, we generalize the original work and show that the computational power of the BN can be extended to compute an arbitrary 1-D map. Also, we describe two possible circuit models of the BN: the programmable unijunction transistor oscillator neuron, which was introduced in the original work as a circuit model of the BN, and the integrated-circuit relaxation oscillator neuron (IRON), which was developed for more precise modeling of the BN. To demonstrate the computational power of the BN, we use the IRON to generate the bifurcation diagrams of the sine-circle map, the logistic map, as well as the tent map, and then compare them with exact numerical versions. The programming of the BN to compute an arbitrary map can be done simply by changing the waveform of the driving signal, which is given to the BN externally; this feature makes the circuit models of the BN especially useful in the circuit implementation of a network of 1-D maps