Modeling brain electrical activity in epilepsy by reaction-diffusion cellular neural networks

2005 ◽  
Author(s):  
F. Gollas ◽  
R. Tetzlaff
Keyword(s):  
Neural Networks ◽  
Electrical Activity ◽  
Reaction Diffusion ◽  
Cellular Neural Networks ◽  
Brain Electrical Activity

CELLULAR NEURAL NETWORKS (CNN) WITH LINEAR WEIGHT FUNCTIONS FOR A PREDICTION OF EPILEPTIC SEIZURES

2003 ◽  
Vol 13 (06) ◽  
pp. 489-498 ◽  
Author(s):  
R. TETZLAFF ◽  
R. KUNZ ◽  
C. NIEDERHÖFER
Keyword(s):  
Neural Networks ◽  
Electrical Activity ◽  
Epileptic Seizures ◽  
Cellular Neural Networks ◽  
Pattern Detection ◽  
Weight Functions ◽  
Brain Electrical Activity ◽  
Detection Algorithms ◽  
Novel Approach

In this paper, we present a novel approach to the prediction of epileptic seizures using boolean CNN with linear weight functions. Three different binary pattern occurrence behaviours will be discussed and analysed for several invasive recordings of brain electrical activity. Furthermore analogic binary pattern detection algorithms will be introduced for a possible prediction of epileptic seizures.

SPATIO-TEMPORAL DYNAMICS OF BRAIN ELECTRICAL ACTIVITY IN EPILEPSY: ANALYSIS WITH CELLULAR NEURAL NETWORKS (CNNs)

2003 ◽  
Vol 12 (06) ◽  
pp. 825-844 ◽  
Author(s):  
R. KUNZ ◽  
R. TETZLAFF
Keyword(s):  
Neural Networks ◽  
Electrical Activity ◽  
Correlation Dimension ◽  
Temporal Dynamics ◽  
Cellular Neural Networks ◽  
Brain Electrical Activity ◽  
Accurate Approximation ◽  
Spatio Temporal

In this contribution a new procedure is proposed for the analysis of the spatio-temporal dynamics of brain electrical activity in epilepsy. Recent investigations1–3 have clarified that changes of estimates of the effective correlation dimension D2(k,m) from successive data segments allow a characterization of the epileptogenic process. These results provide important information for diagnostical purposes and enable a prediction of seizures in many cases. It will be shown that an accurate approximation of [Formula: see text] can be obtained by Cellular Neural Networks (CNNs),4,5 which form a unified paradigm. Moreover, the type of CNN presented here is optimized with respect to future implementations as VLSI realizations.6

scholarly journals Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guowei Yang ◽  
Yonggui Kao ◽  
Changhong Wang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Matrix Theory ◽  
Sufficient Conditions ◽  
Neumann Boundary ◽  
Reaction Diffusion ◽  
Cellular Neural Networks ◽  
Impulsive Effect ◽  
Time Varying ◽  
Delayed Reaction

This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. By using delay differential inequality,M-matrix theory, and analytic methods, some new sufficient conditions ensuring global exponential stability of the periodic FIRDDCNN model with Neumann boundary conditions are established, and the exponential convergence rate index is estimated. The differentiability of the time-varying delays is not needed. An example is presented to demonstrate the efficiency and effectiveness of the obtained results.

COMPLEXITY OF REACTION–DIFFUSION CNN

2006 ◽  
Vol 16 (09) ◽  
pp. 2499-2527 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Energy Balance ◽  
Reaction Diffusion ◽  
Cellular Neural Networks ◽  
Emergence Of Complexity ◽  
Balance Property ◽  
Divergence Property

The emergence of complexity is investigated from the viewpoint of the energy balance property and the divergence property of reaction–diffusion cellular neural networks.

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