4G3 Computational Neuroscience

Aims

The aims of the course are to:

• develop an understanding of the fundamentals of reinforcement learning, and how they relate to neural and behavioural data on the ways in which the brain learns from rewards.
• demonstrate the importance of internal models in neural computations, and provide examples for their behavioural and neural signatures.
• introduce alternative ways of modelling single neurons, and the way these single neuron models can be integrated into models of neural networks.
• explain how the dynamical interactions between neurons give rise to emergent phenomena at the level of neural circuits.
• 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.
• demonstrate case studies of computational functions that neural networks can implement.

Objectives

As specific objectives, by the end of the course students should be able to:

• 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.