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A new design of Spiking Neural Networks is proposed and fabricated using a 0.35 μm CMOS technology. The architecture is based on the use of both digital and analog circuitry. The digital circuitry is dedicated to the inter-neuron communication while the analog part implements the internal non-linear behavior associated to spiking neurons. The main advantages of the proposed system are the small area of integration with respect to digital solutions, its implementation using a standard CMOS process only and the reliability of the inter-neuron communication.

The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). The measurements obtained reveal that chaotic neural ensembles are excellent transmission and convolution systems since mutual information between signals is minimized. At the same time, synchronized cells (that can be understood as ordered states of the brain) can be associated to more complex nonlinear computations. In this sense, we experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. We also suggest that the high-level adaptive mechanisms of the brain that are the Hebbian and non-Hebbian learning rules can be understood as processes devoted to generate the appropriate clustering of both synchronized and chaotic ensembles. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (nonlinear processing for synchronized states and information convolution and parallelization for chaotic).

An inductorless realization of nonautonomous (Murali–Lakshmanan–Chua) MLC chaotic circuit is proposed. The main purpose of this study is to improve the performance of nonautonomous MLC chaotic circuit using only CFOAs. CFOA-based topology used in this realization enables to simulate floating inductance. This modification provides to the designer wideband chaotic signals for secure communication systems. In addition to this major improvement, a CFOA-based nonlinear resistor was used in the new realization of nonautonomous MLC chaotic circuit. The usage of CFOA-based inductance simulator and nonlinear resistor in the circuit structure reduces the component count and provides isolated outputs. The performance of the proposed inductorless nonautonomous chaotic circuit is demonstrated with both PSpice simulations and experimental results.

The noise-like chaotic signal can be generated with very simple nonlinear circuits, and has broad bandwidth and aperiodic properties. These characteristics have drawn considerable attention in the radar community. In the past, the chaotic signal or its modulated version serves as a transmitting signal, and the traditional correlation-type receiver is used for processing. In this sense, the chaotic signal acts as a radar waveform in the noise signal radar, and hence the performance advantages are not distinct. Here, we present a scheme for processing chaotic radar signals. We find a simple relation between the target parameters (range and velocity) and the system parameters of chaos-generating system. With this relation, the measurement of the target parameters is transformed into estimation of the system parameters from the radar return signals. Equipped with high resolution parameter estimation techniques, the proposed principles provide a way to develop high resolution noise signal radars.

The well-known Matsumoto–Chua–Kobayashi (MCK) circuit is of significance for studying hyperchaos, since it was the first experimental observation of hyperchaos from a real physical system. In this paper, we discuss the existence of hyperchaos in this circuit by virtue of topological horseshoe theory. The two disjoint compact subsets producing a horseshoe found in a specific 3D cross-section, both expand in two directions under the fourth Poincaré return map, this fact means that there exists hyperchaos in the circuit.

In this paper, the generation of multiscroll chaotic attractors derived from a time-delay differential equation is presented. The proposed system is represented by only one first-order differential equation including time-delayed state variable, and employs hysteresis function as the nonlinear characteristic. The generalization of the introduced system is based on adding multihysteresis nonlinear characteristic which leads to n-scroll chaotic attractors. The circuit implementation of the proposed system and some experimental results referring to two-, three-, four-, and five-scroll chaotic attractors are reported.

The experimental study of in-out intermittency during the incomplete synchronization of two coupled, nonlinear, autonomous, fourth-order, chaotic, circuits is reported. The two circuits are unidirectionally coupled via a linear resistor. The dependence of laminar lengths, on the deviation of the control parameter from its critical value, and the mean laminar length distribution, are studied.

It has been argued that Lyapunov exponents as a measure of predictability are of limited value because they only provide a global average. Characterizing an attractor by a distribution of times for initial uncertainties to increase by a factor of q has been suggested as a more useful alternative. These have found favor in some applications, despite assumptions of the fictitious perfect model scenario. Here, an electronic circuit, which offers a good test-bed for addressing predictability in the imperfect model scenario, is presented. A novel measure of predictability is presented and implications of model imperfection on characterizing the dynamics of chaotic systems are discussed. It is a case for a multiple model approach.

In this paper, we introduce a new chaotic system and its corresponding circuit. This system has a special property of having a hidden attractor. Systems with hidden attractors are newly introduced and barely investigated. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. We use a geometry-based cost function to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian Mixture Model (GMM) which is fitted to the observed attractor generated by the real system in state space. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. The results show the adequacy of the proposed cost function.