ENGINEERING TRIPOS PART IIB – 2012/2013

Module 4G3 - Computational Neuroscience (not running 2011/12)

Leader:	Dr M Lengyel (ml468@eng)
Timing:	Lent Term
Prerequisites:	none (3G2 or 3G3 recommended)
Structure:	16 lectures
Assessment:	Material / Format / Timing / Marks Lecture Syllabus / Coursework 100 %

AIMS

The course covers basic topics in computational neuroscience, and demonstrates how mathematical analysis and ideas from dynamical systems, machine learning, optimal control, and probabilistic inference can be applied to gain insight into the workings of biological nervous systems. The course also highlights a number of real-world computational problems that need to be tackled by any ‘intelligent’ system, as well as the solutions that biology offers to some of these problems.

Specific aims of this module are to:

• introduce alternative ways of modelling single neurons, and the way these single neuron models can be integrated into models of neural networks,
• describe the challenges posed by neural coding and decoding, and the computational methods that can be applied to study them,
• demonstrate case studies of computational functions that neural networks can implement,
• describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system,
• consider control tasks (sensorimotor and other) faced and solved by the nervous system,
• examine the energy efficiency of neural computations.

Further details and online resources

SYLLABUS

Principles of Computational Neuroscience (9L, M. Lengyel, CUED)

• how is neural activity generated? mechanistic neuron models
• how to predict neural activity? descriptive neuron models
• what should neurons do? normative neuron models
• how to read neural activity? neural decoding
• what happens when many neurons are connected? neural networks
• how do neurons reconfigure their connections? plasticity
• how to tell a neural network what to do? supervised learning
• how can neuronal networks learn without being told what to do? unsupervised learning
• how do neural networks remember? auto-associative memory
• how can our brains achieve the goal of life? reinforcement learning

Representational learning (3L, Dr. R. Turner)

• Bayesian inference and learning
• generative models and receptive fields

Computational sensorimotor control (2L, D. Wolpert, CUED)

• Optimality principles of sensorimotor integration
• Optimality principles of feedback control

Energy aspects of neural computation (2L, S. Laughlin, Zoology)

• energetics of information processing
• the energetic cost of spikes and synapses

OBJECTIVES

By the end of the course students will:

• understand how neurons, and networks of neurons can be modelled in a biomimetic way, and how a systematic simplification of these models can be used to gain deeper insight into them,
• develop an overview of how certain computational problems can be mapped onto neural architectures that solve them,
• recognise the essential role of learning is the organisation of biological nervous systems,
• appreciate the ways in which the nervous system is different from man-made intelligent systems, and their implications for engineering as well as neuroscience.

REFERENCES

Please see the Booklist for Group G Courses for references for this module.

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Last updated: September 2012

teaching-office@eng.cam.ac.uk