Neuronal Information Processing From Biological Data To Modelling And Application
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Neuronal Information Processing, From Biological Data To Modelling And Application
Recent developments in the neurosciences have considerably modified our knowledge of both the operating modes of neurons and information processing in the cortex. Multi-unit recordings have enabled temporal correlations to be detected, within temporal windows of the order of 1ms. Oscillations corresponding to a quasi-periodic spike-giving, synchronized over several visual cortical areas, have been observed in anaesthesized cats and monkeys. Recent studies have also focused on the role played by the dendritic arborization.These developments have led to considerable interest in a coding scheme which relies on precise spatio-temporal patterns from both the theoretical and experimental points of view. This prompts us to look into new models for information processing which will proceed, for example, from a synchronous detection of correlated spike giving, and is particularly robust against noise. Such models could bring about original technical applications for information processing and control.Further developments in this field may be of major importance for our understanding of the basic mechanisms of perception and cognition. They could also lead to new concepts in applications directed towards artificial perception and pattern recognition. Up to now, artificial systems for pattern recognition are far from reaching the standards of human vision. Systems based on a temporal coding by spikes may now be expected to bring about major improvements in this field.This book covers the lectures delivered at a summer school on neuronal information processing. It includes information on all the above-mentioned developments, and also provides the reader with the state-of-the-art in every relevant field, including the neurosciences, physics, mathematics, and information and control theory.
Neural Information Processing
Author: Bao-Liang Lu
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-10-26
The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition.
Artificial Neural Networks as Models of Neural Information Processing
Author: Marcel van Gerven
language: en
Publisher: Frontiers Media SA
Release Date: 2018-02-01
Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.