Training the Next generation
TeachingOur students learn about sensory neuroscience from many angles through our range of degree courses and interdisciplinary modules
A Centre for Learning
Our teaching portfolio
Innovative Teaching Tools
To improve student experience and provide hands-on training in aspects of neuroscience and computation that are difficult to transmit in theoretical lectures alone, we complement our wide-randing teaching portfolio with constant developments in teaching technology. For example, we designed an in-silico model neuron “Spikeling” which allows students a hands-on experience with neuronal signal transmission, information encoding and key electrophysiological concepts without the need of setting up animal experiments. This is now used worldwide, and forms an integral part of our UG module on Sensory Function and Computation.
Other teaching tools along these lines are constantly being developed and tested in collaboration with our Making lab (see tools section).
Junior Research Associates
Sussex University runs a “Junior Research Associate” (JRA) scheme for undergraduate students in the summer before their final year of study. JRA students are funded to join a research group for 8 weeks and get involved in a real research project. The scheme is targeted at those students who are interested in a research career. SNAC faculty would be delighted to hear from students interested in JRAs.
Some of our current PhD students…
Biomimetic models of visual navigation
Fabian Steinbeck
In his pursuit of developing technologies and understanding living nature, Fabian studied a Biomimetics B.Sc. in Bocholt, Germany, with Prof. Tobias Seidl. There, the focus of his studies were the process of transferring knowledge from biological mechanisms to engineered technologies. For his bachelor thesis, he went to study echolocation in bats with Prof. Jim Simmons at Brown University, RI, USA. This added neuroscience to his interests, after which he did his master degree in Neuroscience in Bielefeld, Germany. Both these experiences lead him to join Paul Graham at Sussex, who offered a PhD project which would join both Biomimetics and Neuroscience into one: Neuromimetics. The focus of this project is to develop navigation algorithms inspired by insect-brains and –behaviours. The focus of Fabian’s project is the relationship of sensory signal reliability to the generation of movements. Inspired by a rather understudied insect brain region, the Lateral Accessory Lobe (the insect analog to the mammalian basal ganglia), he developed a new searching algorithm, attempting to explain some of the displayed insect behaviours and brain functions. Especially exciting is the association of this project with the Brains on Board project: an attempt to develop a “whole insect brain model”, putting together various insect navigation models to create a robust but adaptable navigation AI.
Check out Fabian’s recent paper here: Steinbeck et al. 2021 JEB
Light-processing at the first synapse of vision
Tessa Herzog
Tessa’s project seeks to understand how light signals are transmitted across the first synapse of the vertebrate eye, from cone-photoreceptors to bipolar and horizontal cells. Only signals that are transmitted can contribute to vision, everything else will be lost. Accordingly, it is critical to use a system that is simultaneously high-bandwith and reliable. But synapses in general are notoriously unreliable – how can this be reconciled?
To address these kinds of questions, Tessa employs in vivo 2-photon imaging of light-driven synaptic release from zebrafish cone-photoreceptors. This specific combination of model system and technology are currently the only way to directly look at these synapses in action in a live animal!
This allows Tessa to directly ask how reliable the synapse is, how fast it is, how it adapts over repeated presentations of the same stimulus, and many more fundamental questions. Here, preliminary findings suggest that amazingly, in the case of zebrafish red-cones which underpin “black and white” vision, reliability and temporal precision are both extremely high – if you give the synapse the same stimulus 10 times, you get 10 times exactly the same response! Tessa now explores the mechanistic underpinnings of these properties, also delving into nanometer scale serial section electron microscopy data and computational modeling.