PhD thesis available: Spiking Neural Networks

[Sander Bohte]

Dear Connectionists,

My PhD thesis on Computation with Asynchronous 
Spiking Neural Networks 
is now available on the WWW at

http://www.cwi.nl/~sbohte/pub_thesis.htm

For those interested, I have appended the index

Best regards,

Sander Bohte

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Keywords: spiking neural networks, spike time coding, sparse distributed
coding, unsupervised clustering, supervised learning, dynamic feature
binding, higher-order correlations.

Index:
1. Introduction
     1.1 Artificial Neural Networks
     1.2 Computing with asynchronous spike-times 
2. Unsupervised Clustering with Spiking Neurons by Sparse Temporal
Coding and Multi-Layer RBF Networks
     2.1 Introduction 
     2.2 Networks of delayed spiking neurons
     2.3 Encoding continuous input variables in spike-times
     2.4 Clustering with Receptive Fields
     2.5 Hierarchical clustering in a multi-layer network
     2.6 Complex clusters
     2.7 Discussion and Conclusions
3. Error-Backpropagation in Temporally Encoded Networks of Spiking
Neurons
     3.1 Introduction
     3.2 Error-backpropagation
     3.3 The XOR-problem
     3.4 Other Benchmark Problems
     3.5 Discussion
     3.6 Conclusion
4. A Framework for Position-invariant Detection of Feature-conjunctions
     4.1 Introduction
     4.2 Local Computation with Distributed Encodings
     4.3 Implementation
     4.4 Experiments
     4.5 Discussion
     4.6 Conclusions
5. Formal Specification of Invariant Feature-conjunction Detection
     5.1 Introduction
     5.2 Formal Description
     5.3 Conclusion
6. The effects of pair-wise and higher order correlations on the firing
rate of a post-synaptic neuron
     6.1 Introduction
     6.2 Mathematical Solution of the three-neuron problem
     6.3 Calculating the Distribution with N Identical Neurons
     6.4 An artificial neural network
     6.5 Discussion
     6.6 Conclusion
7. The Biology of Spiking Neurons
     7.1 Real Neurons Spike
     7.2 Precision and Reliability of Real Spikes