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Computational neuroscience: neural signal processing & neural computing

  • 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

The human brain is one of the most complex systems known to man. It consists of vast numbers of interacting units (called neurones) that communicate with each other using sequences of pulses (called spike trains). The highest levels of processing in the human brain occur in the neocortex. One mm3 in the neocortex contains around 100,00 cells, 4 km of wiring (called axons) 500m of cell body outgrowths (called dendrites) and up to 109 connections (called synpases) between cells. Much is known about the organisation and function of the human brain. It is possible to study the brain experimentally and to simulate the behaviour of neurones using digital computers. A characteristic feature of all neural systems is that they are intrinsically noisy. Any signal processing techniques used to study neural communications must take this into account.

Such a complex system provides plenty of scope for research projects in Computational Neuroscience. Important areas are the analysis and modelling of neural systems. There is a need to develop multivariate statistical signal processing methods that can be applied to neurophysiological data sets to extract information about the structure, function and operation of the nervous system. This can be supported by a programme of computational modelling using computer models to simulate neural behaviour. These models can be detailed biophysical models or more general purpose bio-inspired models. Simulation can be done in software or hardware (using FPGA technology). Precise project details will depend on your interests. Data is available from existing collaborators using MEA (multi electrode array) technologies or from ongoing work at the York Neuroimaging Centre.

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