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Computational neuroscience: Statistical signal processing for multivariate neuronal data, Neural computing with Spiking neural networks.

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

How does the human brain work? As you read this text, the pixels on your screen are converted into a series of spike trains passing along the neural pathways in your brain, allowing you to understand the content. The human brain is one of the most complex systems known to man. Much is known about the structure and function of the human brain: It consists of vast numbers of interacting units (neurons) that communicate with each other using sequences of pulses (spike trains). The highest levels of processing in the human brain occur in the neocortex, it is estimated that the human neocortex contains around 10 billion neurons with 60 trillion connections between neurons.

This complex system provides plenty of scope for research projects in Computational Neuroscience. Potential projects fall under two broad themes:
1. Development and application of statistical signal processing techniques to study multivariate neuronal recordings.
2. Computer modelling of Spiking Neural Networks (SNNs) to investigate fundamental mechanisms in neural coding, neural computing and information processing in the brain.

Theme 1: Neural and statistical signal processing.
Neuroscience is regarded by many as “data rich”, in our case this means access to large multivariate data sets of neuronal recordings that can be used to understand brain function in health and disease. These data sets consist of different types of data ranging from single unit recordings of individual neurons, to local field potential recordings of localised electrical activity and electroencephalographic (EEG) recordings from the surface of the scalp. A feature of these recordings is that they are intrinsically noisy and non-stationary or time varying. There is a need to develop multivariate statistical signal processing methods that can be applied to these neurophysiological data sets to extract information about the structure, function and operation of the nervous system. A wide range of data sets are available to support this work, from collaborators using multi electrode array (MEA) technologies, human electrophysiological studies using EEG and from Neuroimaging centres using whole head magnetoencephalographic (MEG) systems. A focus of the group is on control of movement, data sets consisting of upper and lower limb electromyographic (EMG) recordings are available to support this research. Projects can focus on studying fundamental mechanisms related to neural function, or can take a more clinically focused approach, for example comparing EEG-EMG interactions in controls against patient groups (e.g. stroke or Parkinson’s disease).

Theme 2: Neural computing with spiking neural networks.
The ability to simulate the electrical signalling processes in the brain is central to developing new models of brain function, and new approaches to neuromorphic computing. This computational modelling uses computer models of single neurons and interconnected networks of these to capture the spatial-temporal dynamics seen in the living brain. A wide range of approaches can be used, for example using detailed biophysical models to capture the dynamics of how an individual neuron, or a small group of neurons, responds to different activation patterns. Alternatively, larger networks using bio-inspired models (e.g. LIF and variants, Izhikevich) can be used to study neural coding and neural computing at the population level. A number of different approaches to learning can be applied, for example Spike Time Dependent Plasticity (STDP) or Reinforcement Learning (RL) in SNNS. Depending on interests and experience, simulations can be done in software (e.g. MATLAB, C/C++, Python) or hardware (e.g. FPGA technology). Research in this area is closely related to Neuromorphic computing, which aims to develop new brain inspired computing paradigms based on the use of SNNs. This more technologically focused view allows projects to consider how our understanding of the behaviour of SNNs can be used in novel computational tasks, for example control of mobile robotic devices.

Projects can readily combine aspects of both of these themes, for example simulated cortical neuron networks can provide data to validate novel statistical signal processing approaches to study neuronal interactions and neuronal connectivity.

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