NEU Neural Computing MSc Natural Computation Department of Computer Science University of York

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!

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.

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

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

Content 1: Biological networks Cerebellar network

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?

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?

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 ms). The firing times are marked by short vertical bars. From Kr ü ger and Aiple (1988).Kr ü ger and Aiple (1988)

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

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.