Computational Neuroscience (UG)

Module aims

    • To provide students with an introduction to Computational Neuroscience
    • To provide students with an appreciation of the role of computational and theoretical approaches to understanding the nervous system
    • To provide students with some practical skills, such as basics knowledge of MATLAB, of numerical tools to simulate

Learning outcomes

Learning Outcomes - Knowledge and Understanding

  • Understanding key concepts concerning the electrical properties underlying information processing in the nervous system, how networks of individual neurons can together perform computations, and how systems of such networks operate together to solve tasks such as visual perception and motor behaviour.
  • To understand how recorded physiological signals can help us to understand how these systems work, and how engineering and statistical approaches help in the analysis of such data.
  • Appreciation of the current level of development of the field of Computational Neuroscience.
  • Appreciation of the relationship between behavioural control and underlying neuronal principles.

Learning Outcomes - Intellectual Skills

  • Levels of description in modelling in neuroscience
  • Derivation of algorithms and quantities used in theoretical neuroscience

Learning Outcomes - Practical Skills

  • Basic knowledge of neuroscience simulation in MATLAB
  • Writing code to simulate basic computational models

Learning Outcomes - Transferable Skills

  • Problem solving and Organisational aquired during the three course works

Module syllabus

  • Definition and Scope: What is Computational Neuroscience? Different approaches to modelling
  • Neuron models. Electrical properties of neurons: the cell membrane, integrate-and-fire and conductance-based neuron models, spike rate adaptation, refractoriness, phase-plane analysis of neuron model dynamics, the Hodgkin & Huxley model.
  • Signal Integration: synaptic integration, neuron computation, synaptic physiology, synaptic transmission (channels), synaptic models.
  • Feedforward and Recurrent networks: the perceptron, multilayer networks, balanced network, ring model, rate-based network, spike-based network.
  • Learning and Memory: synaptic plasticity (unsupervised (rate-based, LTP/LTD, STDP) in primary sensory area, supervised learning in cerebellum, reinforcement learning in basal ganglia).

Pre-requisites

Basic linear algebra: vector operations (vector sum, scalar product etc.), matrix and matrix vector multiplication, matrix inversion Basic calculus: differentiation, integration, differential equations, stability analysis, phase plane analysis, bifurcation analysis, basic dynamical systems. Basics of probability theory: random variables, probabilities, probability distributions, joint, marginal and conditional probabilities, Gaussian distribution, stochastic processes Basics of matlab programming: variables, conditional expressions (e.g. if-then-else), loops (e.g. for, while), functions, basic mathematical expressions

Teaching methods

Lectures: 19 hours
Study groups: 9 hours

Assessments

Examinations:
Written exam: 1 hour, 100% of final mark

No type of previous exam answers or solutions will be available


Exam rubric: 1 hour exam, 3 questions, all mandatory. 

Feedback : feedback is given verbally by the GTAs during the 9h sessions.

Module leaders

Professor Claudia Clopath