Bruce MacLennan, PhD
Office: Min Kao 550
Office Hours: 4:45–6:00 WF, or make an
Office: Min Kao 204
Hours: TR 3:30–5:00 or make an appointment
Email: zmahoor at utk.edu
Classes: 2:30–3:20 MWF in Min Kao 524
Directory of Handouts, Labs, etc.
This page: http://web.eecs.utk.edu/~mclennan/Classes/420
COSC 420 and COSC 527 focus on biologically-inspired computation,
including recent developments in computational methods inspired by
nature, such as neural networks, genetic algorithms and other
evolutionary computation systems, ant swarm optimization, artificial
immune systems, swarm intelligence, cellular automata, and
Fundamental to the understanding and implementation of massively
parallel, distributed computational systems is an investigation of
the behavior and self-organization of a variety of systems in
which useful work emerges from the interaction of many simple
agents. The question we address is: How should a multitude
of independent computational (or robotic) agents cooperate in
order to process information and achieve their goals, in a way
that is efficient, self-optimizing, adaptive, and robust in the
face of changing needs, damage, and attack?
Fortunately, nature provides many models from which we can
learn. In this course we will discuss natural computational
systems that solve some of the same problems that we want to
solve, including adaptive path minimization by ants, wasp and
termite nest building, army ant raiding, fish schooling and bird
flocking, pattern formation in animal coats, coordinated
cooperation in slime molds, synchronized firefly flashing, soft
constraint satisfaction in spin glasses, evolution by natural
selection, game theory and the evolution of cooperation,
computation at the edge of chaos, and information processing in
You will learn about specific computational applications of these
ideas, including artificial neural networks, simulated annealing,
cellular automata, ant colony optimization, artificial immune
systems, particle swarm optimization, and genetic algorithms and
other evolutionary computation systems. These techniques are
also used in computer games and computer animation.
Since the goal of this goal of this course is for you to gain an
intuitive understanding of adaptive and self-organizing
computational systems, the lectures make extensive use of videos,
simulations, and other computer demonstrations. Your grade
will be based on about six or seven moderate-sized projects, and I
will consider group projects (but you will have to do
more!). There are no other assignments or tests for COSC
420 (but there are for COSC 527!).
COSC 527 “Biologically-Inspired Computation” is approved for the
Graduate Minor in Computational Science (IGMCS).
This is a project-oriented course and therefore all students will be
expected to have basic programming skills; for undergraduate CS
students, the recommended
background is completion of the core courses. Since this is
a senior-level CS course, I will expect you to be competent
programmers. For non-EECS students (e.g., those in
biology, ecology, psychology, etc.) I will provide alternate
non-programming assignments. If you have any questions about whether
you should take this course, please send me
Your grade will be based on about the projects, in which you will
conduct and write up experiments using the software
associated with Flake’s book, as well as conducting experiments
with software that you program yourself. (Non-EECS students
can do alternative, non-programming assignments.)
For COSC 420 there will be no exams or other homework.
Students taking COSC 527 (i.e. the course for graduate credit)
will be expected to do specified additional work.
If you meet all the requirements of a project, you will generally
get a B on it. Higher grades (B+, A–, A) are awarded for exemplary
work. If you do not meet the project requirements, or your project
is late, you will get a grade lower than B on that project.
There will be a 10% deduction for each day late, up to 3 days;
after that your grade will be an F.
COSC 420 & 527: Flake, Gary William. The
Computational Beauty of Nature. MIT Press, 1998.
See also the book’s online
webpage (including software).
1. Students will
be able to explain basic concepts of dynamical systems.
2. Students will
be able to explain how biological systems exploit natural
3. Students will
be able to explain how large numbers of agents can self-organize
4. Students will
be able to explain how complex and functional high-level
phenomena can emerge from low-level interactions.
5. Students will
explain how computational processes can be derived from natural
6. Students will
be able to implement simple bio-inspired algorithms.
7. Students will
be able to design and conduct experiments to investigate
empirically bio-inspired systems.
Evolving List of Topics
Chapter numbers refer to Flake unless otherwise specified.
Slides for each lecture will be posted in the course of the
semester. (Slides from the Spring 2012, Fall 2010, Fall 2009, Fall 2008, Fall 2007, Fall 2004 and Fall 2003 versions of
the course are still available on their websites.) Note: An
“*” indicates that the slides were revised after class.
We will do about one topic every two weeks or so.
- Overview: course
description, definition of biologically-inspired computing, why
it is important
Part I [pdf]
- Spatial Models:
Wolfram’s classification, Langton’s lambda, CA models in nature,
excitable media (ch. 15)
Part II.A — CAs* [pdf]
Part II.B — Pattern Formation* [pdf]
Part II.C — Slime Mold [pdf]
Part II.D — Excitable Media [pdf]
Part II.E — Segmentation [pdf]
- Natural and Analog
Computation: artificial neural nets, associative
memory, Hebbian learning, Hopfield networks (ch. 18)
Part III.A — Hopfield Network [pdf]
Part III.B — Stochastic Networks [pdf]
- Neural Networks and Learning:
pattern classification & linear separability, single- and
multilayer perceptrons, backpropagation, internal representation
Part IV.A — Artificial Neural Net Learning [pdf]
Part IV.B — Biological Neural Networks [pdf (70MB)]
- Evolutionary Programming:
biological adaptation & evolution, genetic algorithms,
schema theorem (ch. 20)
Part V.A — Genetic Algorithms [pdf (6/page), pdf (1/page)]
Part V.B — Thermodynamics, Life, and Evolution [pdf (6/page), pdf (1/page)]
- Autonomous Agents and
Self-Organization: termites, ants, flocks, herds, and
schools (ch. 16)
Part VI.A — Flocks, Herds, and Schools [pdf (6/page), pdf (1/page)]
Part VI.B — Ants (Real and Artificial) [pdf (6/page), pdf (1/page)]
Part VI.C — Nest Building* [pdf (6/page), pdf (1/page)]
- Competition and Cooperation:
zero- and nonzero-sum games, iterated prisoner’s dilemma, stable
strategies, ecological & spatial models (ch. 17)
Part VII.A — The Iterated Prisoners’ Dilemma [pdf (6/page), pdf (1/page)]
Part VII.B — Synchronization [pdf (6/page), pdf (1/page)]
Part VII.C — Thermodynamics, Life, and Evolution [pdf (6/page), pdf (1/page)]
- Review of Key Concepts
Part VIII — Review of Key Concepts [pdf (6/page), pdf (1/page)]
is due Feb. 6; please see the Project
Additional helpful information is available at Kristy Van
Hornweder’s website <web.eecs.utk.edu/~kvanhorn/cs594_bio/project1/ca.html>.
If you want to use my Experimental Record, it is also
- Project 2 is due Feb. 20; please see
the Project Description [pdf].
helpful information is available at Kristy’s website <web.eecs.utk.edu/~kvanhorn/cs594_bio/project2/aica.html>.
- Project 3 is due Mar. 8;
please see the Project Description [html].
See Kristy’s web page <web.eecs.utk.edu/~kvanhorn/cs594_bio/project3/hopfieldnet.html>
additional information, hints, tips, etc.
- Project 4 is due Apr. 1; please see the
Project Description [pdf].
See Kristy’s web page <web.eecs.utk.edu/~kvanhorn/cs594_bio/project4/backprop.html>
the data for Problems 1 and 2 and for additional information,
hints, tips, etc.
- Project 5 is due Apr. 15;
please see the Project Description [pdf].
Additional helpful information is available at Kristy’s
- Project 6 is due Apr. 29; please see the
Project Description [pdf].
Additional helpful information is available at
Kristy’s website <web.eecs.utk.edu/~kvanhorn/cs594_bio/project6/pso.html>.
- Extra Credit Project 7 (for extra credit) is due May 6; please see the
Project Description [pdf].
You can use the provided spreadsheet [xls, ods] to record
results, if you want. There is also an example,
partly filled out. For the project you will use the SIPD-async simulator.
- CBN Programs
You can run most of these as applets from the Website
for Flake’s textbook. You can also download sources
and executable from there. Some of these are already
available in the experiments/CBN
- NetLogo Programs
You are supposed to be able to run NetLogo programs as Java
applets. To do this, click on their name below.
However, they don’t seem to run on all browsers (see information
with programs). Also beware that if you are running
NetLogo over a slow connection, it will have to download the
NetLogoLite jar (3.3MB). If you have downloaded a NetLogo
system, you can also download the programs (.nlogo files)
directly from the NetLogo directory.
- Ant Foraging —
simplified simulation of any trail formation during foraging
- Life — Conway’s classic Game
- CA 1D
General Totalistic — 1D totalistic CA simulator (good
for Project 1)
- AICA — activation-inhibition
cellular automaton for pattern formation
- Pattern — pattern
formation by diffusing activation/inhibition
— continuous-time activator/inhibitor system
- Segmentation —
Clock-and-wavefront model of segmentation (somitogenesis) in
- B-Z Reaction —
Belousov-Zhabotinsky reaction (equivalent to Hodgepodge
- SlimeSpiral — spiral
aggregation of slime molds
- SlimeSpiralBig —
spiral aggregation of slime molds (focused on fewer
- SlimeStream —
streaming aggregation of slime molds
- SIPD-async —
spatial iterated prisoner's dilemma
- The following are NetLogo 4.1 programs, which I’m gradually
converting to NetLogo 5.0, after which the older versions will
be removed. Most of them will run without change on NetLogo 5.0.
- The following are NetLogo 4.0 programs, which I’m gradually
converting to NetLogo 4.1, after which the older versions will
- Fur — uses small and large
neighborhoods for activation-inhibition system
- Perceptron —
demonstration of perceptron learning
- Termites — simulation
of Resnick “turmites”
- Pillars — simulation
of Deneubourg model of pillar construction by termites
- The following are NetLogo 3.1.5 programs. If you want to
run these on your own computer, you will have to download the
NetLogo 3.1.5 system, which is not the latest version.
Soon I will put the NetLogo 4.0 versions of these programs
on the website, which will run under the latest version of
- Vants — Langton’s
Vants (virtual ants)
— Langton’s Vants on a large field (may take too much
— generalized vants, with a programmable rule
- Ants — simulation of
- Firefly —
Camazine’s firefly synchronization model
— Wilensky’s firefly synchronization model
- Flocking —
implementation Reynold’s “boids” flocking model
- EIPD — ecological
simulation of Iterated Prisoner’s Dilemma
- SIPD — spatial
simulation of Iterated Prisoner’s Dilemma
Return to MacLennan’s
to Bruce MacLennan / MacLennan@eecs.utk.edu
Last updated: 2013-04-26.