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Computational Neuroscience Course (4G3)

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If you have a question about this talk, please contact Dr Máté Lengyel.

4G3 course -- repeats Wednesdays at 12noon, Thursday at 2pm during Lent 2013

This is the computational neuroscience module taught by Máté Lengyel, Rich Turner, Daniel Wolpert (Engineering), and Simon Laughlin (Zoology). The main audience is 4th year (MEng) students in Engineering, PhD students, and others interested in learning about computational neuroscience.

The course demonstrates how mathematical analysis and ideas from engineering-related disciplines (dynamical systems, signal processing, machine learning, optimal control, and probabilistic inference) can be applied to gain insight into the workings of the nervous system. Knowledge of basic mathematical concepts is assumed.

Topics covered include neural coding, neural networks, plasticity, associative memory, efficient coding, supervised, unsupervised, and reinforcement learning, energy efficiency, and optimal control.

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This course is open to anyone wishing to attend, subject to space constraints.

This talk is part of the Computational Neuroscience series.

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