MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS

2009 ◽  
Vol 19 (11) ◽  
pp. 3605-3656 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cellular Automaton ◽  
Discrete Time ◽  
Cellular Neural Network ◽  
Brain Functions ◽  
Rsa Algorithm ◽  
Logical Operations ◽  
Almost All ◽  
Higher Brain Functions

In this paper, we design a cellular automaton and a discrete-time cellular neural network (DTCNN) using nonlinear passive memristors. They can perform a number of applications, such as logical operations, image processing operations, complex behaviors, higher brain functions, RSA algorithm, etc. By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of memristor cellular automaton. Furthermore, it can perform more than one function at the same time, that is, it allows multitasking.

CELLULAR NEURAL NETWORKS: ASYMMETRIC SPACE-DEPENDENT TEMPLATES, MOSAIC PATTERNS, AND SPATIAL CHAOS

2004 ◽  
Vol 14 (08) ◽  
pp. 2655-2665 ◽  
Author(s):  
LARRY TURYN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hexagonal Lattice ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Integer Lattice ◽  
Spatial Entropy ◽  
Spatial Chaos

We consider a Cellular Neural Network (CNN), with a bias term, on the integer lattice ℤ2in the plane ℝ2. Space-dependent, asymmetric couplings (templates) appropriate for CNN in the hexagonal lattice on ℝ2are studied. We characterize the mosaic patterns and study their spatial entropy. It appears that for this problem, asymmetry of the template has a more robust effect on the spatial entropy than does the sign of a parameter in the templates.

scholarly journals The Gliocentric Brain

2018 ◽  
Vol 19 (10) ◽  
pp. 3033 ◽  
Author(s):  
James Robertson
Keyword(s):  
Neural Networks ◽  
Great Majority ◽  
Memory Storage ◽  
Neural Cells ◽  
Strict Adherence ◽  
Brain Functions ◽  
The Past ◽  
Mechanisms Of Memory ◽  
Higher Brain Functions ◽  
Abnormal Brain

The Neuron Doctrine, the cornerstone of research on normal and abnormal brain functions for over a century, has failed to discern the basis of complex cognitive functions. The location and mechanisms of memory storage and recall, consciousness, and learning, remain enigmatic. The purpose of this article is to critically review the Neuron Doctrine in light of empirical data over the past three decades. Similarly, the central role of the synapse and associated neural networks, as well as ancillary hypotheses, such as gamma synchrony and cortical minicolumns, are critically examined. It is concluded that each is fundamentally flawed and that, over the past three decades, the study of non-neuronal cells, particularly astrocytes, has shown that virtually all functions ascribed to neurons are largely the result of direct or indirect actions of glia continuously interacting with neurons and neural networks. Recognition of non-neural cells in higher brain functions is extremely important. The strict adherence of purely neurocentric ideas, deeply ingrained in the great majority of neuroscientists, remains a detriment to understanding normal and abnormal brain functions. By broadening brain information processing beyond neurons, progress in understanding higher level brain functions, as well as neurodegenerative and neurodevelopmental disorders, will progress beyond the impasse that has been evident for decades.

scholarly journals The necessity of studying higher brain functions from a first-person frame of reference

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 173
Author(s):  
Kunjumon I. Vadakkan
Keyword(s):  
Gold Standard ◽  
Frame Of Reference ◽  
First Person ◽  
Brain Functions ◽  
Third Person ◽  
Experimental Approaches ◽  
Engineered Systems ◽  
Almost All ◽  
Higher Brain Functions

Almost all higher brain functions are first-person properties and anyone seeking to study them faces significant difficulties. Since a third-person experimenter cannot access first-person properties, current investigations are limited to examining the latter by using third-person observations that are carried out at various levels. This limits the current studies to correlational experiments using third-person observed findings. In order to initiate a study of explanations for the first-person properties, experimental approaches should be undertaken from the first-person frame of reference. But, there is a huge barrier. I discuss my opinion for crossing this barrier using a three-stage approach – theoretical, computational and experimental – in that order. These stages will naturally lead to the gold standard of understanding the mechanism by replicating it in engineered systems. The hurdles and incentives of undertaking this approach are discussed.

scholarly journals Discrete-time Cohen-Grossberg neural networks with transmission delays and impulses

2009 ◽  
Vol 43 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Sannay Mohamad ◽  
Haydar Akça ◽  
Valéry Covachev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Discrete Time ◽  
Continuous Time ◽  
Global Exponential Stability ◽  
Exponential Convergence ◽  
Unique Equilibrium ◽  
Transmission Delays

Abstract A discrete-time analogue is formulated for an impulsive Cohen- -Grossberg neural network with transmission delay in a manner in which the global exponential stability characterisitics of a unique equilibrium point of the network are preserved. The formulation is based on extending the existing semidiscretization method that has been implemented for computer simulations of neural networks with linear stabilizing feedback terms. The exponential convergence in the p-norm of the analogue towards the unique equilibrium point is analysed by exploiting an appropriate Lyapunov sequence and properties of an M-matrix. The main result yields a Lyapunov exponent that involves the magnitude and frequency of the impulses. One can use the result for deriving the exponential stability of non-impulsive discrete-time neural networks, and also for simulating the exponential stability of impulsive and non-impulsive continuous-time networks.

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