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MSc in Natural Computation: Exploring Neural Systems at University of York

This module offers a comprehensive foundation in neural systems, focusing on both theoretical concepts and practical applications. Students will explore biological and artificial neural networks, emphasizing the design and implementation of neural architectures. Learning outcomes include identifying suitable neural systems for specific tasks, designing data encodings, and evaluating architecture applications. With an emphasis on hands-on MATLAB exercises, students gain practical experience while understanding complex neural behaviors without delving into statistical learning theories.

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MSc in Natural Computation: Exploring Neural Systems at University of York

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  1. NEUNeural Computing MSc Natural Computation Department of Computer Science University of York

  2. Module description • “provides a foundation of theoretical and practical knowledge in the subject of neural systems” • Algorithms inspired by natural neural systems • Biological (natural) neural systems and the principal artificial neural architectures • Emphasis will be on the characterisation of the artificial systems, rather than the analysis of their properties in statistical terms. • ..so no statistical learning theory!

  3. Learning outcomes • On completion of this module students will be able to • Identify which neural system is suitable for a particular task. • Design, implement and experiment with neural architectures for a particular task. • Design appropriate encodings of data. • Evaluate the application of a particular architecture to a given problem.

  4. Who is it aimed at? • Basic computer science experience of algorithms and complexity will be assumed • No biological background will be necessary. • Some discussion later of “realistic” neuron models, but not in depth

  5. Level of mathematics required • Calculus, matrices and vectors • If you can follow these, that’s good • If you can’t, some bits of theory will be missing

  6. Content 1: Biological networks Cerebellar network

  7. Content 2: Feed forward networks • Start with the simplest system – one neuron performing one operation – what can it do? • We can make more complex arrangements of neurons, in which we have layers with connections from one layer to the next – what does this add to their capabilities? • We can also change the operation of the neuron • How do we decide on the architecture for a given problem?

  8. Content 3: Recurrent networks • Instead of a flow from inputs to outputs, we can have more arbitrary (or complete) connections – the flow of information can be around a loop = recurrent or dynamic • Designate some nodes as inputs and others as outputs, or all nodes are inputs at one time and outputs at a later time • What sort of behaviour do we get from recurrent networks? • What are the issues with storage and stability?

  9. Content 4: Spiking networks • So far we have though about signals in and out – voltage, current, or just numbers • In reality, neurons are not quite like that. One difference is in spiking behaviour Spatio-temporal pulse pattern. The spikes of 30 neurons (A1-E6, plotted along the vertical axes) are shown as a function of time (horizontal axis, total time is 4 000 ms). The firing times are marked by short vertical bars. From Krüger and Aiple (1988).

  10. Practical elements • In addition to lecture material, there will be exercises to do in your own time. • That will usually require some work with MATLAB to model simple neural systems. • We won’t be using the MATLAB Neural Network toolbox, because that hides the details of the algorithms

  11. Assessment • The assessment for the module is open • The assessment will consist of some or all of the following: • Demonstration of understanding of lecture material • Selection and application of algorithms to given datasets • Analysis of the output of specific algorithms • Review of the literature on a particular topic.

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