Evolving Probabilistic Spiking Neural Networks
β Scribed by Nuttapod Nuntalid
- Publisher
- Auckland University of Technology
- Year
- 2012
- Tongue
- English
- Leaves
- 246
- Category
- Library
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
<p><p>Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original authorβs contribution to the area. The book introduc
It is open how neurons in the brain are able to learn without supervision to discriminate between spatio-temporal firing patterns of presynaptic neurons. We show that a known unsupervised learning algorithm, Slow Feature Analysis (SFA), is able to acquire the classification capability of Fisherβs Li
<p>While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied --- perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a