Brain-like Information Processing for Spatio-Temporal Pattern Recognition

Abstract

Information processes in the brain, such as gene and protein expression, learning, memory, perception, cognition, consciousness are all spatio- and/or spectro temporal. Modelling such processes would require sophisticated information science methods and the best ones could be the brain-inspired ones, that use the same brain information processing principles. Spatio and spectro-temporal data (SSTD) are also the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organization and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. Brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems is potentially capable of modeling complex information processes due to the ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organizing manner. This chapter reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN), and computational neurogenetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data-analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.