PY1SN-Introduction to Systems Neuroscience

Module Provider: Psychology
Number of credits: 20 [10 ECTS credits]
Level:4
Terms in which taught: Autumn / Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2016/7

Module Convenor: Prof Ingo Bojak

Email: i.bojak@reading.ac.uk

Summary module description:
Introduction to Neuroscience, Mathematics and Computer Science

Aims:
Students will be introduced to the basics of Systems Neuroscience from a computational perspective, which will provide the necessary background for the module “CS2NN16 Neural Networks”. This involves learning in three different fields – neuroscience, mathematics and computer science – and fusing them together into a series of practical applications. In neuroscience, we will consider models of neuron from the simplest individual one to networks of neurons involved in memory formation. We will also analyse brain connectivity and neuroimaging signals. In mathematics, we will treat matrices, differential equations and various numerical methods. In computer science, we will use the C++ language and learn coding from simple loops to object-oriented design. We will also use Matlab to visualise our results. All these elements come together in six practical labs where students will program models and carry out data analyses.

Assessable learning outcomes:
By the end of the module the student will be able to:
•Understand and construct computational models of single neurons and their networks
•Be able to perform brain connectivity and power spectral analyses
•Show knowledge of appropriate mathematical tools for model building and data analysis
•Demonstrate working knowledge of C++ in the context of scientific computing

Additional outcomes:
Students will acquire the necessary background to successfully engage with the module “CS2NN16 Neural Networks” offered by the department of Computer Science.

Outline content:
The module includes topics such as the following:
•Neuroscience: brain connectivity, M/EEG & fMRI signal generation, leaky integrate and fire neurons, conductance models, cable equation, multi-compartment models, firing-rate models, recurrent networks, associative memory
•Mathematics: ordinary and partial differential equation systems and their linearization, forward and backward Euler methods, eigenvalues and -vectors, fixed points, discrete Fourier transforms, power spectral densities
•Computer Science: using the C++ & Matlab user interfaces, program structure, data types, screen & file I/O, loops, arrays, function/subroutine calls, control structures, pointers, dynamic memory allocation, specification (.h) files, external libraries, object-oriented design, inheritance, direct integration of C++ with Matlab

Brief description of teaching and learning methods:
(a) Lectures covering the basic theoretical and technical content as outlined above, each accompanied by a set of short questions / exercises reviewing the lecture content.

(b) Computer labs where students will work through a set of prepared exercises, which will be submitted at the end of each lab. Students then will be given a small computational project building on these preceding exercises, which they will work on independently, and submit later.

(c) A review session at the end of the term in which the content of lectures and labs will be discussed in a questions and answers format. Every student will have to submit a number of questions prior to the session based on their own independent review of the taught material.

N.B. The contact hours in the table below are indicative of the contact hours for students studying this module in the UK, and may vary for students taking this module at branch campuses.

Contact hours:
  Autumn Spring Summer
Lectures 12 12
Seminars 2 2
Practicals classes and workshops 6 6
Guided independent study 80 80
       
Total hours by term 100.00 100.00
       
Total hours for module 200.00

Summative Assessment Methods:
Method Percentage
Written assignment including essay 10
Report 36
Project output other than dissertation 30
Class test administered by School 24

Other information on summative assessment:
This module is assessed entirely by coursework (100% of the marks for the module):
•One set of exercises / questions per two hours of lectures (6 per term, 12 for the whole module), each constituting 2%, for a total of 24%.
•6 submissions of lab work, 3 per term, each contributing 6%, for a total of 36%.
•6 lab-based small projects, 3 per term, each providing 5%, for a total of 30%.
•2 submissions of review questions, 1 per term, each contributing 5%, for a total of 10%.

Formative assessment methods:

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

  • where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

  • The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
    You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

    Length of examination:

    Requirements for a pass:
    A mark of 40% overall

    Reassessment arrangements:
    Re-assessment is by the submission of a computational project of appropriate difficulty, set by the module convenor and teaching team.

    Last updated: 13 April 2016

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