Acknowledgements Foreword 1. Introduction 2. Adaptation and contraction theory for the synchronization of complex neural networks 3. Temporal Coding is not only about Cooperation - it is also about Competition 4. Using Non-Oscillatory Dynamics to Disambiguate Simultaneous Patterns 5. Functional constraints on network topology via generalized sparse representations 6. Evolution of Time in Neural Networks: From the Present to the Past, and Forward to the Future 7. Synchronization of Coupled Pulse-Type Hardware Neuron Models for CPG Model 8. A Universal Abstract-Time Platform for Real-Time Neural Networks 9. Solving Complex Control Tasks via Simple Rule(s): Using Chaotic Dynamics in a Recurrent Neural Network Model 10. Time scale analysis of neuronal ensemble data used to feed neural network models 11. Simultaneous EEG-fMRI: Integrating Spatial and Temporal Resolution
A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps , and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs.
The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EE
The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems
Develops a unified view of how the time domain can be effectively employed in neural network models
Proposes that conceptual models of neural interaction are required in order to understand the data being collected