Farhat, Nabil H
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Publication Self-Organization in a Parametrically Coupled Logistic Map Network: A Model for Information Processing in the Visual Cortex(2009-03-31) Pashaie, Ramin; Farhat, Nabil HIn 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.Publication Analog Realization of Arbitrary One-Dimensional Maps(2003-12-01) Del Moral Hernandez, Emilio; Lee, Geehyuk; Farhat, Nabil HAn 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.Publication Cort-X II: Low Power Element Design of a Large-Scale Spatio-Temporaral Pattern Clustering System(2007-05-01) Yuan, Jie; Farhat, Nabil H; Song, Ning; Van der Spiegel, JanComplex spatio-temporal patterns can be clustered using a network of parametrically coupled logistic maps. This paper describes the processing element design of such a Cort-X system. Each Cort-X element consists of a non-linear coupling (LC) and a non-linear dynamic element (IRON). The circuits are designed for low-power operation and to be robust against process variations. This has been accomplished by using openloop circuits, and a self-calibration technique that compensate for process variations. The circuits were implemented in a 0.25 um, 2.5V CMOS process and consumes a total of 12mW of power at 1MHz which is about a factor of 20 less power than previous realizations. This opens the possibility for building a large-scale Cort-X system on a chip for the recognition of complex spatio-temporal patterns.Publication GBOPCAD: A Synthesis Tool for High-Performance Gain-Boosted Opamp Design(2005-08-01) Yuan, Jie; Farhat, Nabil H; Van der Spiegel, JanA systematic design methodology for high-performance gain-boosted opamps (GBOs) is presented. The methodology allows the optimization of the GBO in terms of ac response and settling performance and is incorporated into an automatic computer-aided design (CAD) tool, called GBOPCAD. Analytic equations and heuristics are first used by GBOPCAD to obtain a sizing solution close to the global optimum. Then, simulated annealings are used by GBOPCAD to find the global optimum. A sample opamp is designed by this tool in a 0.6-μm CMOS process. It achieves a dc gain of 80 dB, a unity-gain bandwidth of 836 MHz with 60o phase margin and a 0.0244% settling time of 5 ns. The sample/hold front-end of a 12-bit 50-MSample/s analog–digital converter was implemented with this opamp. It achieves a signal-to-noise ratio of 81.9 dB for a 8.1-MHz input signal.Publication A CMOS Monolithic Implementation of a Nonliniear Interconnection Module for a Corticonic Network(2006-05-01) Farhat, Nabil H; Yuan, Jie; Van der Spiegel, JanA 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.Publication Background Calibration With Piecewise Linearized Error Model for CMOS Pipeline A/D Converter(2008-02-01) Farhat, Nabil H; Yuan, Jie; Van der Spiegel, JanA new all-digital background calibration method, using a piecewise linear model to estimate the stage error pattern, is presented. The method corrects both linear and nonlinear errors. The proposed procedure converges in a few milliseconds and requires low hardware overhead, without the need of a high-capacity ROM or RAM. The calibration procedure is tested on a 0.6- µm CMOS pipeline analog-to-digital converter (ADC), which suffers from a high degree of nonlinear errors. The calibration gives improvements of 17 and 26 dB for signal-noise-and-distortion ratio (SNDR) and spurious-free dynamic range (SFDR), respectively, for the Nyquist input signal at the sampling rate of 33 MSample/s. The calibrated ADC achieves SNDR of 70.3 dB and SFDR of 81.3 dB at 33 MSample/s, which results in a resolution of about 12 b.Publication Realization of Receptive Fields with Excitatory and Inhibitory Responses on Equilibrium-State Luminescence of Electron Trapping Material Thin Film(2007-06-01) Pashaie, Ramin; Farhat, Nabil HOur 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.Publication An analytic model for the dynamics of electron trapping materials with applications in nonlinear optical signal processing(2008-01-01) Pashaie, Ramin; Farhat, Nabil HIn 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.Publication Optical Realization of the Retinal Ganglion Receptive Fields in Electron-Trapping Material Thin Film(2006-04-01) Pashaie, Ramin; Farhat, Nabil HOptical 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.Publication Corticonic models of brain mechanisms underlying cognition and intelligence(2007-08-14) Farhat, Nabil HThe 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.