SPIKEFRAME: A framework for learning rules in networks of spiking neural networks
In the project we worked on constructing a framework for the formulation of goal-oriented learning rules for spiking neural networks.
Existing algorithms do not relate to each other, use different terminologies and notations without making connection to each other. We aimed at developing a mathematical and conceptual framework within which goal-oriented learning algorithms which could be formulated clearly and concisely for ease of their application, understanding and generalisation.
This was important for the understanding of how neuron-scale plasticity "conspires" to bring about goal-oriented human and animal learning behaviours at the cognitive and behavioural scale.
Lecturer in Computing
his project was a part of Subproject 4 Theoretical Neuroscience of the Human Brain Project, a multi-billion EU Horizon 2020 Flagship project, and involved collaboration with the European Institute of Theoretical Neuroscience (EITN) in Paris (the hub of the Subproject 4).