Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.Comment: Preprin

Exploratory Analysis of Connected Fully Autonomous Vehicles on the Safety and Efficiency of Road Networks using Microsimulation

The research had set out to explore the effects of the widespread introduction of driverless technology by using publicly available data and assessing the changes it brings to the efficiency and safety of the road network. ConFAVs were slowly introduced to the network and average vehicle delays and the level of service (LOS) of links observed, followed by a surrogate safety assessment. Two published behaviour models (Atkins and CoEXist), and a third model (Tested Logic) was created, which accounted for a change in ConFAV behaviour while following another ConFAV. A comparison of the change in the average vehicle delay and the total number of serious conflicts recorded, highlighted that the CoEXist behavioural model had performed the best in three types of junctions and was used to further analyse the case study. The case study involved 2 small, isolated networks within the Queen Elizabeth Olympic Park Area of London (‘Site A’ was residential and ‘Site B’ was commercial). ‘Site A’ performed well with delays but performed poorly when comparing the number of recorded conflicts against the increasing numbers of ConFAVs. ‘Site B’ showed limited improvement in LOS and performed poorly in the safety analysis as the number of recorded conflicts increased fourfold in some scenarios. The results of the case study led to a conclusion that increased numbers of ConFAVs driving in platoons within the network could reduce delays and as a result either maintained the LOS of the chosen route or made it better. The lead vehicle in the platoon was able to anticipate changes in signals and communicate this with the trailing vehicles, allowing them to perform better at signalised junctions. Platoons also increased network capacity on congested links allowing better performance in the average delays, as observed in Case Study B. However, greater numbers of platoons resulted in larger numbers of rear-end conflicts when a surrogate safety analysis was performed using Time to Collision (TTC) as a parameter. Thus, it was recommended that another method is used to investigate potential conflicts that could recognise and account for platoons