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Description
This lecture-based module examines how behaviour results from the properties of neurons and synapses in the brain. Some simple computational models of how networks of neurons can be used to perform useful functions are introduced and applied to help understand several examples of the neural bases of behaviour in humans and animals. Topics covered will include the role of synaptic plasticity in learning and memory, the coding of information by the firing rate and time of firing of neurons, the neural bases of memory, coordination of action, audition, olfaction and conscious awareness. Neural systems studies will include the motor, parietal and frontal cortices, the hippocampus, cerebellum and the spinal cord.
The module is an option for MSci/BSc Neuroscience student and for students on other degree programmes with a strong Neuroscience background and at least A level maths.
Indicative lecture list:
- Introduction to artificial neural networks & unsupervised learning.
- Artificial neural networks, feedback & simple supervised learning.
- More advanced learning algorithms in artificial neural networks.
- Computational properties of individual neurons
- The hippocampus and spatial representation
- Path integration, continuous attractors and grid cells
- Models of conscious awareness
- Hippocampal and striatal navigation.
- Reinforcement learning
- Spatial processing in the spine and motor cortex.
- Hippocampus and associative memory
- Student presentations of research papers
- Learning, performing and remembering serially ordered actions.
- Student presentations of research papers
- Temporal processing in audition and olfaction.
- Computing with spike timing and delays; course review.
鈥淔undamentals of Computational Neuroscience鈥� by Thomas Trappenberg (OUP) gives a general overview of the field.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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