A cross-disciplinary team of researchers from the Universities of Stirling, York, Cardiff, Manchester, and Southampton are working together on an EPSRC-funded project, Edgy Organism, to develop a novel end-to-end neuromorphic design approach drawing inspiration from how data is processed and represented in the brain and build an efficient hardware architecture based on spiking neural networks. The project aims to develop novel computing solutions, that can autonomously and reliably detect illegal or harmful activities in crowded public spaces, with minimum intrusion of personal space and privacy. We are recruiting a team of outstanding researchers from Visual Neuroscience, Psychology, Edge Computing, AI/ML, and Neuromorphic Engineering, to work with us on achieving Edgy Organism project’s ambitious objectives. As part of this project, Psychology, Faculty of Natural Sciences, University of Stirling is offering a fixed term (27 months) full-time Postdoctoral Research Fellow position to work with Dr Elena Gheorghiu and the cross-disciplinary team of researchers.

Vision begins in the eye, and what the “retina tells the brain” is a major interest in visual neuroscience. To deduce what the retina encodes (“tells”), computational models are essential. The most important models in the retina currently aim to understand the responses of the retinal output neurons – the ganglion cells. Typically, these models make simplifying assumptions about the neurons in the retinal network upstream of ganglion cells. One important assumption is linear spatial integration. In this talk, I first define what it means for a neuron to be spatially linear or nonlinear and how we can experimentally measure these phenomena. Next, I introduce the neurons upstream to retinal ganglion cells, with focus on bipolar cells, which are the connecting elements between the photoreceptors (input to the retinal network) and the ganglion cells (output). This pivotal position makes bipolar cells an interesting target to study the assumption of linear spatial integration, yet due to their location buried in the middle of the retina it is challenging to measure their neural activity. Here, I present bipolar cell data where I ask whether the spatial linearity holds under artificial and natural visual stimuli. Through diverse analyses and computational models, I show that bipolar cells are more complex than previously thought and that they can already act as nonlinear processing elements at the level of their somatic membrane potential. Furthermore, through pharmacology and current measurements, I illustrate that the observed spatial nonlinearity arises at the excitatory inputs to bipolar cells. In the final part of my talk, I address the functional relevance of the nonlinearities in bipolar cells through combined recordings of bipolar and ganglion cells and I show that the nonlinearities in bipolar cells provide high spatial sensitivity to downstream ganglion cells. Overall, I demonstrate that simple linear assumptions do not always apply and more complex models are needed to describe what the retina “tells” the brain.

Zebrafish and cichlids are popular models in visual neuroscience, due to their amenability to advanced research tools and their diverse set of visually guided behaviours. It is often asserted that animals’ neural systems are adapted to the statistical regularities in their natural environments, but relatively little is known about the visual spatiotemporal features in the underwater habitats that nurtured these fish. To address this gap, we have embarked on an examination of underwater habitats in northeastern India and Lake Tanganyika (Zambia), where zebrafish and cichlids are native. In this talk, we will describe the methods used to conduct a series of field measurements and generate a large and diverse dataset of these underwater habitats. We will present preliminary results suggesting that the demands for visually-guided navigation differ between these underwater habitats and the terrestrial habitats characteristic of other model species.