Harnessing Spiking Neural Networks for Enhanced Situational Awareness

Spiking Neural Networks (SNNs) are a kind of artificial neural network (ANN) intended to more closely model natural neural networks in human brains. Information flows through the network based on the gradual accumulation of 'electrical charge' over time, giving SNNs a way of modelling temporal-based processes as well as having a kind of embedded memory. Importantly, SNNs can be implemented on hardware that requires far less power consumption than 'traditional' kinds of ANN, as well as being trainable on much smaller datasets.
The aim of this artificial intelligence PhD is to explore the question of how SNN approaches may be used to model tasks involving machine understanding of situations, and prediction of future states. The objectives are, starting with relatively simple temporal problems, to explore specific domains including understanding the dynamics of human social networks and social media, performing activities autonomously, and engaging in human-machine collaboration. The project will work in close collaboration with Crime and Security Institute researchers, using large-scale data sources to investigate the potential of SNNs for the enhancement of situational awareness.