Hierarchy And Dynamics In Neural Networks
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Hierarchy and dynamics in neural networks
Hierarchy is a central feature in the organisation of complex biological systems and particularly the structure and function of neural networks. While other aspects of brain connectivity such as regionalisation, modularity or motif composition have been discussed elsewhere, no detailed analysis has been presented so far on the role of hierarchy and its connection to brain dynamics. Recent discussions among many of our colleagues have shown an increasing interest in hierarchy (of spatial, temporal and dynamic features), and this is an emerging key question in neuroscience as well as generally in the field of network science, due to its links with concepts of control, efficiency and development across scales (e.g. Hilgetag et al. Science, 1996; Ravasz et al. Science, 2002; Bassett et al. PNAS, 2006; Mueller-Linow et al. PLoS Comp. Biol., in press). The proposed Research Topic will address recent findings from a theoretical as well as experimental perspective including contributions under the following four headings: 1) Topology: Detecting and characterizing network hierarchy; 2) Experiments: Neural dynamics across hierarchical scales; 3) Dynamics: Activity spread, oscillations, and synchronization in hierarchical networks; 4) Dynamics: Stable functioning and information processing in hierarchical networks.
Hierarchical Neural Networks for Image Interpretation
Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
Biological Neural Networks: Hierarchical Concept of Brain Function
Author: Konstantin V. Baev
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This book is devoted to a novel conceptual theoretical framework of neuro science and is an attempt to show that we can postulate a very small number of assumptions and utilize their heuristics to explain a very large spectrum of brain phenomena. The major assumption made in this book is that inborn and acquired neural automatisms are generated according to the same func tional principles. Accordingly, the principles that have been revealed experi mentally to govern inborn motor automatisms, such as locomotion and scratching, are used to elucidate the nature of acquired or learned automat isms. This approach allowed me to apply the language of control theory to describe functions of biological neural networks. You, the reader, can judge the logic of the conclusions regarding brain phenomena that the book derives from these assumptions. If you find the argument flawless, one can call it common sense and consider that to be the best praise for a chain of logical conclusions. For the sake of clarity, I have attempted to make this monograph as readable as possible. Special attention has been given to describing some of the concepts of optimal control theory in such a way that it will be under standable to a biologist or physician. I have also included plenty of illustra tive examples and references designed to demonstrate the appropriateness and applicability of these conceptual theoretical notions for the neurosciences.