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Combining negative selection and classification techniques for anomaly detection
A novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection and produces fuzzy characterization of the normal (or abnormal) space. Expand
Beyond Feedforward Models Trained by Backpropagation: A Practical Training Tool for a More Efficient Universal Approximator
This work implemented a generic cellular SRN (CSRN) and applied it for solving two challenging problems: 2-D maze navigation and a subset of the connectedness problem, and superior generalization has been demonstrated in the case of connectedness. Expand
Unsupervised Learning with Self-Organizing Spiking Neural Networks
A hybridization of self-organized map properties with spiking neural networks that retain many of the features of SOMs is presented, and using the optimal choice of parameters, this approach produces improvements over state-of-art spiking Neural networks. Expand
Phase transitions in the neuropercolation model of neural populations with mixed local and non-local interactions
The role of non-local (axonal) connections in generating and modulating phase transitions of collective activity in the neuropil is investigated and a relationship between critical values of the noise level and non-locality parameter to control the onset of phase transitions is derived. Expand
Advances in Neuromorphic Memristor Science and Applications
Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligentExpand
Cellular SRN Trained by Extended Kalman Filter Shows Promise for ADP
This work improves the previous results by training the cellular simultaneous recurrent neural network with extended Kalman filter (EKF), the original EKF algorithm has been slightly modified. Expand
Chaotic Resonance - Methods and Applications for Robust Classification of noisy and Variable Patterns
A theory of stochastic chaos is developed, in which aperiodic outputs with 1/f2 spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise. Expand
Biocomplexity: adaptive behavior in complex stochastic dynamical systems.
A new approach to chaos research is proposed that has the potential of characterizing biological complexity and the resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and Stochastic differential equations. Expand
Breathing as a Fundamental Rhythm of Brain Function
It is argued that respiration, via multiple sensory pathways, contributes a rhythmic component to the ongoing cortical activity and is suggested that this rhythmic activity modulates the temporal organization of cortical neurodynamics, thereby linking higher cortical functions to the process of breathing. Expand
Complete stability analysis of a heuristic approximate dynamic programming control design
This paper provides new stability results for Action-Dependent Heuristic Dynamic Programming (ADHDP), using a control algorithm that iteratively improves an internal model of the external world inExpand