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Introduction to Neural Networks Andy Philippides Centre for Computational Neuroscience and Robotics CCNR School of Cog

Lectures -- 2 per week Time Day Place 12:30 - 1:20 Mon Arun - 401 11:30 - 12:20 Wed Arun - 401 Seminar

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Introduction to Neural Networks Andy Philippides Centre for Computational Neuroscience and Robotics CCNR School of Cog

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    6. Assessment 3rd years: All coursework Masters students: 50% coursework, 50 % exam (start of next term) Coursework is 2 programming projects first is 20% of coursework (details next week) due in week 6, second 80% due week 10. Coursework dealt with in seminars, some theoretical, some practical matlab sessions (programs can be in any language, but matlab is useful for in-built functions) This weeks seminar: light maths revision

    7. Course Texts 1. Haykin S (1999). Neural networks. Prentice Hall International. Excellent but quite heavily mathematical 2. Bishop C (1995). Neural networks for pattern recognition. Oxford: Clarendon Press (good but a bit statistical, not enough dynamical theory) 3. Pattern Classification, John Wiley, 2001 R.O. Duda and P.E. Hart and D.G. Stork 4. Hertz J., Krogh A., and Palmer R.G. Introduction to the theory of neural computation (nice, but somewhat out of date)

    8. 5. Pattern Recognition and Neural Networks by Brian D. Ripley. Cambridge University Press. Jan 1996. ISBN 0 521 46086 7. 6. Neural Networks. An Introduction, Springer-Verlag Berlin, 1991 B. Mueller and J. Reinhardt As its quite a mathematical subject good to find the book that best suits your level Also for algorithms/mathematical detail see Numerical Recipes, Press et al. And appendices of Duda, Hart and Stork and Bishop

    10. What are biological NNs? UNITs: nerve cells called neurons, many different types and are extremely complex around 1011 neurons in the brain (depending on counting technique) each with 103 connections INTERACTIONs: signal is conveyed by action potentials, interactions could be chemical (release or receive neurotransmitters) or electrical at the synapse STRUCTUREs: feedforward and feedback and self-activation recurrent

    13. We now know its not quite that simple Single neurons are highly complex electrochemical devices Synaptically connected networks are only part of the story Many forms of interneuron communication now known acting over many different spatial and temporal scales

    28. Artificial Neural Networks (ANNs) A network with interactions, an attempt to mimic the brain UNITs: artificial neuron (linear or nonlinear input-output unit), small numbers, typically less than a few hundred INTERACTIONs: encoded by weights, how strong a neuron affects others STRUCTUREs: can be feedforward, feedback or recurrent

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