Three fully-funded BBSRC MIBTP PhD Studentships in Computational Neuroscience are available at the Centre for Systems Neuroscience, the University of Leicester (UK). To apply, please visit: https://le.ac.uk/study/research-degrees/funded-opportunities/bbsrc-mibtp <https://www.google.com/url?q=https%3A%2F%2Fle.ac.uk%2Fstudy%2Fresearch-degrees%2Ffunded-opportunities%2Fbbsrc-mibtp&sa=D&sntz=1&usg=AFQjCNG-qJUxXw32RK_EYllUbkInuOO9rA> https://sites.google.com/site/jiankliu/join-us *The application deadline is 12 January 2020.* *1. Neuronal coupling across spatiotemporal scales and dimensions of cortical population activity* * Dr. Michael Okun and Dr. Jian Liu, CSN/NPB, University of Leicester* https://www.findaphd.com/phds/project/neuronal-coupling-across-spatiotempora... The human cortex is the most complex known system. It is responsible for a vast range of sensorimotor, decision making, and other cognitive abilities of humans and other mammals. The activity of cortical neuronal networks is organised across multiple spatiotemporal scales, and remains poorly understood. Our laboratory is particularly interested in the relationship between the activity of an individual neuron and of the larger networks within which the neuron is embedded (Lewis, 2015). For example, we have recently compared the coupling between neurons and their local network across an extensive range of timescales, finding major timescale-dependent distinctions, suggestive of different mechanisms regulating cortical activity on different timescales (Okun et al., 2019). We use recordings using next-generation high-density silicon probes for data collection (Jun et al., 2017) and advanced computational methods for their analysis. There are several computational projects available in the above research area, relying on data we are collecting in the laboratory as part of ongoing projects, as well as on publicly available datasets. The projects are suitable for students with a background in exact sciences or computer science and programming. *2. Decoding movement kinematics from subpopulations of motor cortex neurons* * Dr. Todor Gerdjikov and Dr. Jian Liu, CSN/NPB, University of Leicester* https://www.findaphd.com/phds/project/decoding-movement-kinematics-from-subp... The purpose of the current project is to investigate novel approaches for decoding movement parameters from neural data acquired from morphologically distinct motor cortex neurons. Using computational approaches we will investigate the relationship between forelimb movement kinetics and neural activity in subpopulations of output-defined motor cortex neurons. Firstly, we will link activity in discrete output-defined M1 neuronal populations to movement parameters in rats trained in a skilled reaching task. This aspect of the work will rely on modern viral approaches to separately tag neurons belonging to different projections and record their activity in behaving rats using fibre photometry and/or extracellular neurophysiology. Computational approaches such as machine learning and neural network modelling will be used to decode kinematics derived from movement data. A second aspect of the work will involve causal experiments where we will use optogenetics to selectively ‘turn off’ the activity of discrete projections. We will investigate how these manipulations affect fine motor control in behaving rats to causally tease apart the contribution of each projection to motor control. *3. Towards a functional model for associate learning and memory formation* * Dr. Jian Liu and Professor Rodrigo Quian Quiroga, CSN/NPB, University of Leicester* https://www.findaphd.com/phds/project/towards-a-functional-model-for-associa... This project aims to study the most up-to-date experimental data regarding single-neuron and network learning and coding in humans, with the expectation that a functional model could be established thereon. If possible, this model shall be not just biologically descriptive but also computationally implementable, as the stimulation protocol is based on the natural scenes of visual and auditory scenes that are beyond the simple protocols. This project involves data analysis where the data of human recordings will be provided by local researchers at our Centre. We will have access to the data of single-cell hippocampal recordings during memory and learning tasks of human subjects under the natural stimulations. Based on these data, we will draw a hypothesised theoretical model that is plausible for these new sets of experimental data as well as for other related published results. Furthermore, designs of new experiments based on the theoretical predictions shall also be made such that the model could be experimentally testified or falsified.