[Comp-neuro] BBSRC MIBTP PhD Studentships in Computational Neuroscience at Leicester

Jian Liu jiankliu at gmail.com
Thu Dec 12 13:17:41 CET 2019


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-spatiotemporal-scales-and-dimensions-of-cortical-population-activity/?p116607


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-subpopulations-of-motor-cortex-neurons/?p116590


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-associate-learning-and-memory-formation/?p116600


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.
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