Coupling, Synchronies and Firing Patterns: from Cognition to Disease
- Alessandro E. P. Villa, Universite Joseph Fourier, Grenoble, FR
The session is aimed towards the understanding of cognitive and pathological brain processes inferred by theoretical, computational and experimental studies of complex temporal activity in barin and neural networks. This concerns particularly those processes associated to changes in functional networks leading to epilepsy and Alzheimer's Disease. In this session we would like to emphasize the role of synchronization as carrier of information with respect to global activity patterns in the brain.
Papers related to the following topics are welcomed:
- Connectionist Models of Alzheimer's Disease
- Temporal analysis of the spike train
- Detection of deterministic chaos in brain
- Forecast of epileptic seizure onset
- Age related changes in topology of neural networks
- Bifurcations in neural dynamics
- Synchronization of neural activity
- Oscillatory activity in cell assemblies
- Coupling of neural networks activity
- Spike timining dependent plasticity
- Spreading of activity waves in the central nervous system
Constructive Neural Networks
- David A. Elizondo, Centre for Computational Intelligence School of Computing, De Montfort University, Leicester, UK
- Leonardo Franco, Dept. of Computer Science, University of Malaga, Spain
- Jose M. Jerez, Dept. of Computer Science, University of Malaga, Spain
This special session will be devoted to constructive neural networks and other incremental learning algorithms that constitute an alternative to the standard method using back-propagation. These algorithms provide an incremental way of building neural networks with reduced topologies for classification problems. Furthermore, these neural networks produce a multilayer topology, which together with the weights, are determined automatically by the constructing algorithm and thus avoid the search for finding a proper neural network architecture. Another advantage of these algorithms is that convergence is guaranteed by the method.
A growing amount of current research in neural networks is oriented towards this important topic. Providing constructive methods for building neural networks can potentially create more compact models which can easily be implemented in hardware and used on embedded systems.
The purpose of this session is to gather together some of the leading investigators and research groups in this growing area, and to provide an overview of the most recent advances on the techniques being developed for constructive neural networks and their applications.
Other topics of interest include methods for estimating what neural architectures are best suited for a given problem, comparisons of different network topologies and genetic or evolving methods for architecture design.
