14,017 research outputs found
CMOS current-mode chaotic neurons
This paper presents two nonlinear CMOS current-mode circuits that implement neuron soma equations for chaotic neural networks, and another circuit to realize programmable current-mode synapse using CMOS-compatible BJT's. They have been fabricated in a double-metal, single-poly 1.6 /spl mu/m CMOS technology and their measured performance reached the expected function and specifications. The neuron soma circuits use a novel, highly accurate CMOS circuit strategy to realize piecewise-linear characteristics in the current-mode domain. Their prototypes obtain reduced area and low voltage power supply (down to 3 V) with clock frequency of 500 kHz. As regard to the synapse circuit, it obtains large linearity and continuous, linear, weight adjustment by exploration of the exponential-law operation of CMOS-BJT's. The full accordance observed between theory and measurements supports the development of future analog VLSI chaotic neural networks to emulate biological systems and advanced computation
Design of time delayed chaotic circuit with threshold controller
A novel time delayed chaotic oscillator exhibiting mono- and double scroll
complex chaotic attractors is designed. This circuit consists of only a few
operational amplifiers and diodes and employs a threshold controller for
flexibility. It efficiently implements a piecewise linear function. The control
of piecewise linear function facilitates controlling the shape of the
attractors. This is demonstrated by constructing the phase portraits of the
attractors through numerical simulations and hardware experiments. Based on
these studies, we find that this circuit can produce multi-scroll chaotic
attractors by just introducing more number of threshold values.Comment: 21 pages, 12 figures; Submitted to IJB
Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation
This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
Adapting Predictive Feedback Chaos Control for Optimal Convergence Speed
Stabilizing unstable periodic orbits in a chaotic invariant set not only
reveals information about its structure but also leads to various interesting
applications. For the successful application of a chaos control scheme,
convergence speed is of crucial importance. Here we present a predictive
feedback chaos control method that adapts a control parameter online to yield
optimal asymptotic convergence speed. We study the adaptive control map both
analytically and numerically and prove that it converges at least linearly to a
value determined by the spectral radius of the control map at the periodic
orbit to be stabilized. The method is easy to implement algorithmically and may
find applications for adaptive online control of biological and engineering
systems.Comment: 21 pages, 6 figure
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