IEEE Member-only icon Nikola Kasabov - EvoSpike - Evolving Probabilistic Spiking Neural Networks and Neuro-Genetic Systems for Spatio- and Spectro-Tem Nikola Kasabov - EvoSpike - Evolving Probabilistic Spiking Neural Networks and Neuro-Genetic Systems for Spatio- and Spectro-Tem

Nikola Kasabov - EvoSpike - Evolving Probabilistic Spiking Neural Networks and Neuro-Genetic Systems for Spatio- and Spectro-Tem

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Abstract: Spatio- and spectro-temporal data (SSTD) are the most common data in many domain areas, including bioinformatics, neuroinformatics, ecology, environment, medicine, engineering, economics, etc. Still there are no sufficient methods to model such data and to discover complex spatio-temporal patterns from it. The brain is functioning as a spatio-temporal information processing machine and brilliantly deals with spatio-temporal data, thus being a natural inspiration for the development of new methods for SSTD. This research aims at the development of new methods for modeling and pattern recognition of SSTD, called evolving probabilistic spiking neural networks (epSNN), along with their applications. epSNN are built on the principles of evolving connectionist systems and eSNN in particular and on probabilistic neuronal models. The latter extent the popular leaky integrate-and-fire spiking model with the introduction of some biologically plausible probabilistic parameters. The epSNN are evolving structures that learn and adapt to new incoming data in a fast incremental way. The research explores several approaches to creating epSNN for SSTD, from a single neuron, to reservoir computing and neuro-genetic systems. A single neuronal model can capture SSTD and it can also generate a precise spike time sequence in response to a SST pattern of spikes from hundreds and thousands of inputs/synapses. The research explores different types of neuronal models and dynamic synapses, including a SPAN model, Fusiâ??s algorithm implemented on the INI Zurich (www.ini.unizh.ch) SNN chip, and a novel stepSNN model that implements the time-to-first spike principle and probabilistic synapses. The research explores further ensembles of neurons and neuronal structures that may be called â??reservoirsâ??. Here they are recurrent SNN that are evolving and deep learning structures, capturing spatial- and temporal components in their interaction and integration. The epSNN spatio-temporal states can be identified and classified for pattern recognition tasks, which is illustrated through some preliminary experiments on gesture- and sign language recognition, moving object recognition, EEG data recognition. The epSNN can learn data in an on-line manner using a frame-based input information representation, or alternatively - an event-address based representation (EAR), the latter implemented in the INI Zurich silicon retina chip and DVS camera and the silicon cochlea chip. The project also explores how epSNN can be used to implement finite automata models and associative memories. A main problem in the EvoSpike model and system development is the optimization of numerous parameters. For this purpose three approaches are proposed: using evolutionary computation methods; using a gene regulatory network (GRN) model, or using both in one system, depending on the application. Linking gene/protein expression to epSNN parameters may also lead to new types of neuron-synapse-astrocydes models inspired by new findings in neuroscience. Neurogenetic models are promising for modeling and prognosis of neurodegenerative diseases such as Alzheimerâ??s disease and for personalized medicine in general. Future research is expected to continue through tighter integration of knowledge and methods from information science, bioinformatics and neuroinformatics. The research is funded by the EU FP7 Marie Curie project, the Knowledge Engineering and Discovery Research Institute KEDRI (www.kedri.info) of the Auckland University of Technology and the Institute for Neuroinformatics, University of Zurich and ETH (INI, www.ini.unizh.ch).

Abstract: Spatio- and spectro-temporal data (SSTD) are the most common data in many domain areas, including bioinformatics, neuroinformatics, ecology, environment, medicine, engineering, economics, etc. Still there are no sufficient methods to model such data and to discover complex spatio-temporal patterns from it. The brain is functioning as a spatio-temporal information processing machine and brilliantly deals with spatio-temporal data, thus being a natural inspiration for the development of new methods for SSTD. This research aims at the development of new methods for modeling and pattern recognition of SSTD, called evolving probabilistic spiking neural networks (epSNN), along with their applications. epSNN are built on the principles of evolving connectionist systems and eSNN in particular and on probabilistic neuronal models. The latter extent the popular leaky integrate-and-fire spiking model with the introduction of some biologically plausible probabilistic parameters. The epSNN are evolving structures that learn and adapt to new incoming data in a fast incremental way. The research explores several approaches to creating epSNN for SSTD, from a single neuron, to reservoir computing and neuro-genetic systems. A single neuronal model can capture SSTD and it can also generate a precise spike time sequence in response to a SST pattern of spikes from hundreds and thousands of inputs/synapses. The research explores different types of neuronal models and dynamic synapses, including a SPAN model, Fusiâ??s algorithm implemented on the INI Zurich (www.ini.unizh.ch) SNN chip, and a novel stepSNN model that implements the time-to-first spike principle and probabilistic synapses. The research explores further ensembles of neurons and neuronal structures that may be called â??reservoirsâ??. Here they are recurrent SNN that are evolving and deep learning structures, capturing spatial- and temporal components in their interaction and integration. The epSNN spatio-temporal states can be identified and classified for pattern recognition tasks, which is illustrated through some preliminary experiments on gesture- and sign language recognition, moving object recognition, EEG data recognition. The epSNN can learn data in an on-line manner using a frame-based input information representation, or alternatively - an event-address based representation (EAR), the latter implemented in the INI Zurich silicon retina chip and DVS camera and the silicon cochlea chip. The project also explores how epSNN can be used to implement finite automata models and associative memories. A main problem in the EvoSpike model and system development is the optimization of numerous parameters. For this purpose three approaches are proposed: using evolutionary computation methods; using a gene regulatory network (GRN) model, or using both in one system, depending on the application. Linking gene/protein expression to epSNN parameters may also lead to new types of neuron-synapse-astrocydes models inspired by new findings in neuroscience. Neurogenetic models are promising for modeling and prognosis of neurodegenerative diseases such as Alzheimerâ??s disease and for personalized medicine in general. Future research is expected to continue through tighter integration of knowledge and methods from information science, bioinformatics and neuroinformatics. The research is funded by the EU FP7 Marie Curie project, the Knowledge Engineering and Discovery Research Institute KEDRI (www.kedri.info) of the Auckland University of Technology and the Institute for Neuroinformatics, University of Zurich and ETH (INI, www.ini.unizh.ch).

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