BI2BI17-Biologically Inspired Computing

Module Provider: School of Biological Sciences
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites: BI1MA17 Mathematics
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Prof Slawomir Nasuto

Email: s.j.nasuto@reading.ac.uk

Type of module:

Summary module description:

In spite of the advances in computing technology naturally occurring systems are still surprising us with the spectrum of complex behaviours they exhibit - pattern recognition, ability to self-repair, robustness to perturbations or noise, and adaptability in face of dynamic and often unpredictable environment. Seemingly simple tasks for natural systems offer state of the art challenges for traditional computation.

Such ability of dealing with complex information has inspired number of researchers to pursue novel computational methods inspired by biological solutions to what seem to be computational problems.

This module covers the theory and implementation of a number of computational systems inspired by biology, including brain inspired artificial neural networks, evolutionary algorithms, swarm intelligence methods based on social organisms, computing instantiated in molecules and cells and biologically inspired pattern formation systems.

Aims:

The module aims to provide basic introduction to foundations of computing as a theoretical framework enabling researcher to describe systems, artificial and natural, that deal with information. It will describe in detail modes of computation inspired by functionality of selected biological systems, namely artificial neural networks, evolutionary algorithms, swarm intelligence, cellular and DNA computing and biological pattern formation.

Assessable learning outcomes:

By the end of the module the student should be able to understand the basic concepts related to information, and computation and also evaluate their limitations in light of the processes and operations performed in biological systems. They will be familiar with the types of nonconventional computing systems inspired by biology and will understand in what context and how they can be applied to 'real-world' problems.

Additional outcomes:

Outline content:

Overview of the fundamentals of computing will be the starting point of the module. Various biologically inspired techniques will be described, for some their implementation is provided, and suitable applications discussed. Techniques examined are grouped by their biological inspiration (and mode of computation they offer): Artificial Neural Network architectures will be mostly focussing on the supervised methods selected from Single and Multi- Layer Perceptrons and associated learning methods; Radial Basis Function Networks; Self Organising Maps, Associative and Recurrent Networks. Algorithms inspired by natural evolution will include Genetic Algorithms and Evolutionary Programming. Swarm intelligence techniques will include Particle Swarm Optimisation, Ant Algorithms and Stochastic Diffusion Search. Methods inspired by cellular biology will be selected from membrane computing, DNA computing and amorphous computing and immune systems. Biologically inspired pattern formation methods will be selected from techniques including Lindenmeyer systems, Cellular Automata and Reaction Diffusion systems.

Brief description of teaching and learning methods:

The module comprises 2Ìýlectures per week.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	20
Guided independent study:	80
Ìý	Ìý	Ìý	Ìý
Total hours by term	100
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	60
Oral assessment and presentation	40

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

The module is 100% coursework based.

First part of the assessment (40% weight) consists of an oral presentation based on a selected research papers and critically discussing recent advances in biologically inspired computing approaches to an interesting biomedical engineering problem.

The second assessment (60% weight) consists of students writing a blog and programming a demo both illustrating student critical thinking and based on aspects of biologically inspired computing going beyond the coursework material.

Formative assessment methods:

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:
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:
40%

Reassessment arrangements:

Examination only. One 2-hour examination paper in August.

Additional Costs (specified where applicable):

Last updated: 8 April 2019