Computational Neuroscience (INFR11209)
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No prior biology/neuroscience knowledge is required. This course requires knowledge of linear algebra, calculus, probability and statistics. In particular, we assume familiarity with vectors and matrices (including matrix inverse and eigenvectors), special functions (logarithm, exponential), integration and differentiation of basic functions, the Taylor expansion, probability distributions (Poisson distribution, univariate and multivariate normal distribution, exponential distribution), expectation and variance of random variables, and Bayesian inference (prior and likelihood, joint and conditional distributions, Bayes rule). We will make use of simple linear differential equations, but prior experience of these is not a prerequisite. Some basic physics concepts will be used (e.g., voltage, capacitance, resistance) but prior knowledge is not required. Basic programming skills (e.g. in Python+NumPy or in Matlab) are required for the tutorials and assessments.
In this course we study computation in neural systems. We will consider problems such as: How do neurons work and how do they communicate with one another? How do groups of neurons work together to form representations of the external world? How are memories stored and retrieved in the brain? We will employ a combination of bottom-up and top-down approaches, meaning that we study these problems both by modelling and simulating the biological hardware, and by taking inspiration from artificial intelligence to try to build theories of the brain.
This course focuses on computation in the nervous system. You will be introduced to basic neuroscience concepts, learn about how computational models are used to simulate processes in the brain, and learn about theories for how the brain processes information and performs computations. Course Content:1. Introduction to basic neuroscience concepts2. Models of neurons3. Neural encoding4. Neural decoding5. Information theory6. Network Models7. Plasticity/learningThe course will be delivered through lectures and computer labs.
Written Exam 75%, Coursework 25%, Practical Exam 0%
Additional Assessment Information
Coursework will involve implementing and/or analysing/discussing in more detail material from lectures.
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