Final Report Summary - EVOSPIKE (Evolving Probabilistic Spiking Neural Networks for Spatio-Temporal Pattern Recognition)
Specifically, there were 12 major achievements:
1. a framework for spatio- and spectro-temporal data (SSTD) modelling and pattern recognition with evolving SNN was developed and published;
2. a new method for SSTD was developed and published - dynamic evolving SNN (deSNN);
3. a new method for SSTD was developed and published - spike pattern association neuron (SPAN);
4. a reservoir type of eSNN for SSTD was developed and published;
5. a pilot application on moving object recognition was developed and published;
6. a pilot application on handwritten digit recognition was developed and published;
7. a pilot application on EEG SSTD was developed and published;
8. a novel computational neuro-genetic model called NeuCube was principally developed and published in LNCS and also submitted as a paper to Nature. NeuCube is superior than other techniques for brain neurogenetic SSTD mining and understanding;
9. a new type of spatio-temporal associative memory and spatio-temporal finite automata were proposed as part of the NeuCube framework;
10. algorithms and programs in Python were developed to run the deSNN and SPAN on the custom analog / digital VLSI hardware (multi-neuron SNN chips) developed in the group of Prof. Indiveri. Preliminary results were included in three joint publications;
11. software was developed and made available, along with all publications, at the project web site: http://ncs.ethz.ch/projects/evospike/;
12. recommendations for further research and long term collabouration in neuromorphic computation were suggested to be based on the NeuCube novel architecture. Two proposals for collabourative work are in a lst stage of preparation - one to IRSES and one to FET-Open to be submitted in January 2013.