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## 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:
Current from: 2018/9

Module Convenor: Prof Ingo Bojak

Email: i.bojak@reading.ac.uk

Type of module:

Summary module description:

Introduction to scientific programming, mathematical modelling and data analysis applied to the neurosciences

Aims:

Students will be introduced to the basics of scientific programming, mathematical modelling and data analysis from a neuroscience perspective, achieving the prerequisite standards for the module “CS2NN16 Neural Networks” in Computer Science. This involves learning in three different fields – neuroscience, mathematics and computer science – but with a clear focus on learning how to program. In neuroscience, we will consider models of neural activity from individual cells to networks of brain areas. In mathematics, we will consider matrices, differential equations and various numerical methods. In computer science, we will learn coding in C++, from loops to libraries. We will also use Matlab and/or Excel to visualise results. All these elements come together in labs and projects, 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 program in C++ at a basic level

• Know how to implement in C++ some mathematical models of neural activity

• Know how to implement in C++ some data analysis methods used in the neurosciences

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, EEG signal generation, leaky integrate and fire neurons, recurrent networks

• Mathematics: differential equation systems, forward Euler method, fixed points, discrete Fourier transforms

• Computer Science: data types, functional decomposition, control structures, specification files, libraries

Brief description of teaching and learning methods:

(a) Lectures covering the content as outlined above, each followed by a set of short questions reviewing content. Interactive Review & Response lectures accompany every content lecture.

(b) Computer labs with prepared exercises, which will be submitted soon after the lab. Students then will work independently on a related computational project, to be submitted before the next lecture.

(c) A Q&A session at the end of the term for which every student will submit a number of questions based on their own review of the taught material.

(d) A series of unmarked "code puzzles" with standard solutions provided to hone programming skills.

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

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

This module is assessed entirely by coursework (100% of the marks for the module):

• One set of questions per content lecture (6 per term, 12 for the whole module), each worth 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:

Weekly Q&A lectures accompanying the content lectures will provide practical and interactive feedback and support for students. A series of "code puzzles" will be provided to the students. These will not be marked, but rather offer an opportunity for students to challenge and check their understanding by comparing their own to the also provided standard solution.

Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (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.

Assessment 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.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 28 September 2018

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.