An Investigation into Learning Methods for Spiking Neural Networks in the Context of Autonomous Navigation

Lead Research Organisation: University of York
Department Name: Electronics


Artificial intelligence is present throughout our everyday lives and has a global reach. A common approach in machine learning is the use of artificial neural networks, used with supervised learning. These have achieved state-of-the-art performance in classification tasks. These networks use analogue neuron models which have simplified dynamics compared to more biophysically accurate neuron models. These more accurate models are known as spiking neuron models, and networks of these are referred to as spiking neural networks (SNNs). The field of SNNs is an area of current research interest with a number of open questions regarding neuronal dynamics, network architecture and learning rules to maximise the performance of these more biologically realistic network models. This project will consider different approach to training spiking neural networks in benchmark problems, with a focus on autonomous navigation. The aim of the project is to explore the use of supervised, unsupervised and semi-supervised learning in SNNs. The objectives are to:
-Review current approaches to modelling SNN dynamics and defining network architecture.
-Identify candidate learning algorithms to apply with a focus on unsupervised and semi-supervised approaches in SNNs.
-To define a benchmark problem in autonomous navigation with consideration of the most appropriate input and output mappings for an SNN control system.
-To plan, undertake and analyse data from a series of experiments exploring the behaviour of the chosen SNN learning algorithms and input-output mappings.
-To report the results in a PhD thesis.

The research methodology will take a bio-inspired approach that maps information from neuroscience into software based implementations that can be applied to real-world problems. The project will provide training in computational neuroscience, machine learning, software development and experimental data analysis. Software will be constructed using both third party software and custom developed software (likely to be in either Python and/or MATLAB). The work will contribute to the development of new AI technologies to complement existing artificial neural network approaches. The project is aligned with EPSRC research themes: ICT and Artificial Intelligence Technologies.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/T518025/1 01/10/2020 30/09/2025
2603096 Studentship EP/T518025/1 01/10/2021 31/03/2025 Gregor Mackenzie