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Details of Grant 

EPSRC Reference: EP/R035806/1
Title: Brain--inspired disinhihbitory learning rule for continual learning tasks in artificial neural networks
Principal Investigator: Clopath, Dr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Dept of Bioengineering
Organisation: Imperial College London
Scheme: EPSRC Fellowship
Starts: 01 January 2019 Ends: 31 December 2023 Value (£): 1,040,501
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Jul 2018 EPSRC ICT Prioritisation Panel July 2018 Announced
04 Sep 2018 ICT and DE Fellowship Interviews 5 and 6 September 2018 Announced
Summary on Grant Application Form
Machines are achieving near-human performance at learning tasks such as image categorisation or speech recognition, but most of the state-of-the-art solutions excel in fixed environments. Systems deployed in real-world scenario, on the other hand, need to be able to learn in changing environments. Why is this a challenge? Every time a typical learning system encounters a new task, it overwrites the solution to previous tasks by what it learns on the new one. Imagine a robot used for elderly care: After two months of training it to carry the person up and down the stairs, there are renovations in the house, and the robot learns to transport the person with a temporary lift. It would be silly if the robot would thereby unlearn its skills for navigating stairs and would have to relearn the stair condition for two months after the renovation. Current state-of-the art machine learning algorithms have this limitation, a challenge called continual learning, or life-long learning.

For this EPSRC Fellowship, we plan to develop a brain-inspired learning algorithm and test it in artificial neural networks solving a continual learning task. So, let's look at how the brain might solve continual learning. Humans have the fascinating ability to adapt to their environment and memorise experiences; both require memory. We can learn quickly and remember for a long time, but this leads to a dilemma: In order to learn quickly, the brain needs to change very easily i.e. be plastic, but in order to remember for a long time, the brain must not be too plastic. The basis of learning and memory at the neural level are changes in the connections between neurons, called synaptic plasticity. Scientists have worked thoroughly to characterise synaptic plasticity, focusing on excitatory neurons, while mostly neglecting the role of inhibitory neurons. We suggest that instead of learning equally across all experience, the solution to the dilemma is to regulate which memories to learn, therefore avoiding unnecessary overwriting of important memories. We propose that inhibition is the key to regulating learning, in that lowering inhibition opens a gate for learning. To test this hypothesis, in this EPSRC Fellowship we will investigate the interaction of excitatory and inhibitory plasticity using computational models in recurrent networks. We will then test whether and how inhibition gates synaptic plasticity and therefore learning. Finally, we will test the performance of our brain-inspired learning rule in a continual learning task of navigation under a reinforcement learning framework.

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Organisation Website: http://www.imperial.ac.uk