A 4-year fully-funded PhD studentship project with Professor Mark Humphries and Professor Stephen Coombes is available for October 2024 start, through the University of Nottingham's BBSRC Doctoral Training Programme. The striatum is central to an extraordinary range of disorders, from Parkinson's disease to OCD, but our best models for its function are outdated and contradicted by recent data. In this project, we will test the hypothesis that the striatum is a special class of recurrent neural networks (RNNs) that use purely inhibitory connections. We will build and analyse this class of networks, deriving predictions for the computations that striatum performs, and for the activity of neuron populations in the striatum. We will then test these predictions in two large-scale datasets of population recordings from striatum in freely-exploring mice from the studies of Klaus et al (Neuron, 2017) and Markowitz et al (Cell, 2018). The DTP offers 2 lab rotations and wide-ranging training modules. If successful, the PhD student will join the Humphries' lab and be part of the School of Psychology's extensive postgraduate support network.

The ability to discriminate different sensory patterns by disentangling their neural representations is an important property of neural networks. While a variety of learning rules are known to be highly effective at fine-tuning synapses to achieve this, less is known about how different cell types in the brain can facilitate this process by providing architectural priors that bias the network towards sparse, selective, and discriminable representations. We studied this by simulating a neuronal network modelled on the dentate gyrus—an area characterised by sparse activity associated with pattern separation in spatial memory tasks. To test the contribution of different cell types to these functions, we presented the model with a wide dynamic range of input patterns and systematically added or removed different circuit elements. We found that recruiting feedback inhibition indirectly via recurrent excitatory neurons proved particularly helpful in disentangling patterns, and show that simple alignment principles for excitatory and inhibitory connections are a highly effective strategy.