I would be interested to know about simple spiking neural network architectures that are implemented to study the neural coding, both temporal and rate-based. Furthermore, I would like to know if the neural learning is somehow influenced by the kind of neural network topology (i.e., the configuration of the synapses).
I need some simple spiking neural network schemes (already studied to solve generic tasks) in order to test the paradigm that we're developing.
I would greatly appreciate any help you can give me. Please, provide me some references.
well i may not be able to give you the code but i can suggest few paper which uses Dynamic evolving spiking neural network.
soon Prof. Kasabov will be releasing a research version of Neucube for matlab.
For an intriguing and biologically realistic example of how learning may depend on the topology of an SNN see "R. Urbanczik, W. Senn. Learning by the dendritic prediction of somatic spiking. Neuron 81(3):521–528, 2014"; http://www.sciencedirect.com/science/article/pii/S0896627313011276
The following paper studies a model of synaptic plasticity in a spiking neural network, and looks at both spike-timing-based and rate-based coding. They argue that the resulting connectivity should reflect the type of code used, which could be relevant to your question.
Clopath, Claudia, et al. "Connectivity reflects coding: a model of voltage-based STDP with homeostasis." Nature neuroscience 13.3 (2010): 344-352.
http://www.nature.com/neuro/journal/v13/n3/full/nn.2479.html
Article Connectivity reflects coding: A model of voltage-based STDP ...
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