Spiking Neural Networks

Sander M. Bohte.

The idea of neural computation with precisely timed spikes in networks of asynchronous spiking neurons is treated in detail in this thesis. We develop and extend algorithms that allow Asynchronous Spiking Neural Networks (ASNN's) to compute in ways traditionally associated with artificial neural networks, like pattern recognition and unsupervised clustering. Additionally, we investigate how spiking neurons could be used for solving the binding-problem: we propose a framework for dynamic feature binding based on the properties of distributed coding with populations of spiking neurons, and we investigate the most likely nature of the synchrony measured in biological systems.

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

The pdf can be found here.