COSC 420/427/527
BiologicallyInspired Computation
Spring 2020
Instructor:
Bruce MacLennan [he/his/him]
Phone: 9740994
Office: Min Kao 550
Office Hours: 2:30–3:30 F and most MW, or make an
appointment
Email:
maclennan AT utk.edu
TAs:
TBD
Office: TBA
Hours: TBA, or make an appointment
Email: TBD AT vols.utk.edu
Classes: 11:15–12:05 MWF in Min Kao 524
Directory of Handouts, Labs, etc.
This page: http://web.eecs.utk.edu/~mclennan/Classes/420527
Information
Description
COSC 420, 427, and 527 focus on biologicallyinspired
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 multiagent systems.
Fundamental to the understanding and implementation of massively
parallel, distributed computational systems is an investigation of
the behavior and selforganization 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, selfoptimizing, 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
the brain.
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 selforganizing
computational systems, the lectures make extensive use of videos,
simulations, and other computer demonstrations. Your grade
will be based on about six or seven moderatesized projects, and I
will consider group projects (but they will have to be more
ambitious!).
COSC 527 “BiologicallyInspired Computation” is approved for the
Interdisciplinary
Graduate Minor in Computational Science (IGMCS).
If you are interested in Artificial Neural Net Learning: take
COSC 425.
If you are interested in Biological Neural Networks: take COSC
421/521.
Prerequisites
This is a projectoriented course and therefore all students will be
expected to have basic programming skills; for undergraduate CS
students, the recommended
minimum background is completion of the core courses. Since
this is a senior/graduatelevel CS course, I will expect you to
be competent programmers and to have the general background of
a senior in CS. For nonEECS students (e.g., those in biology,
ecology, psychology, etc.) I will provide alternate nonprogramming
assignments. If you have any questions about whether you should take
this course, please send me
mail.
Grading
Your grade will be based on the projects, in which you will
conduct and write up experiments using either offtheself
software or software that you program yourself. (NonEECS
students can do alternative, nonprogramming assignments.)
Students taking COSC 427 and COSC 527 will be expected to do
specified additional work beyond what is required for COSC 420.
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.
Late Policy:
There will be a 20% deduction for each day late, up to 2 days;
after that your grade will be an F. No exceptions!
There will be occasional pop quizzes, which together will count
no more than one project.
Text
Flake, Gary William. The Computational Beauty of Nature.
MIT Press, 1998. See also the book’s
online webpage (including software).
Accommodations
 For Students with Disabilities
Students who have a disability that requires accommodation(s)
should make an appointment with the Office of Disability Services
(9746087) to discuss their specific needs as well as schedule an
appointment with me during my office hours.
 Name and Pronoun Accommodations
If you use a name and/or pronouns other than what is in Banner,
please
email me with the name and/or pronouns that you would like
me to use and I will be glad to accommodate this request.
Student Learning
Outcomes
1. Students will
be able to explain basic concepts of dynamical systems.
2. Students will
be able to explain how biological systems exploit natural
processes.
3. Students will
be able to explain how large numbers of agents can selforganize
and adapt.
4. Students will
be able to explain how complex and functional highlevel
phenomena can emerge from lowlevel interactions.
5. Students will
explain how computational processes can be derived from natural
models.
6. Students will
be able to implement simple bioinspired algorithms.
7. Students will
be able to design and conduct experiments to investigate
empirically bioinspired 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. Note: An “*” indicates that the slides were revised after
class.
 Overview: course
description, definition of biologicallyinspired computing, why
it is important
Part I
 Spatial Models:
Wolfram’s classification, Langton’s lambda, CA models in nature,
excitable media (ch. 15)
Part II.A — CAs
Part II.B — Pattern Formation
Part II.C — Slime Mold
Part II.D — Excitable Media
Part II.E — Segmentation
 Recurrent Networks: artificial neural nets, associative
memory, Hebbian learning, Hopfield networks (ch. 18)
Part III.A — Hopfield Network
Part III.B — Stochastic Networks
 Evolutionary Programming:
biological adaptation & evolution, genetic algorithms,
schema theorem (ch. 20)
Part V.A — Genetic Algorithms
Part V.B — Thermodynamics, Life, and
Evolution
 Autonomous Agents and
SelfOrganization: termites, ants, flocks, herds, and
schools (ch. 16)
Part VI.A — Flocks, Herds, and Schools
Part VI.B — Ants (Real and Artificial)
Part VI.C — Nest Building
 Competition and Cooperation:
zero and nonzerosum games, iterated prisoner’s dilemma, stable
strategies, ecological & spatial models (ch. 17)
Part VII.A — The Iterated Prisoners’ Dilemma
Part VII.B — Synchronization
 Review of Key Concepts
Part VIII — Review of Key Concepts
We will do about one topic every two weeks or so.
Projects/Assignments
Simulations
 NetLogo Programs
You can run the following NetLogo 6 programs as Java
applets. To do this, click on their name below. This
works with Firefox and Safari, but not Chrome. If you have
downloaded a NetLogo system,
you can also download the program sources (.nlogo files)
directly from the NetLogo directory or
from the applet pages. [Most of these programs have “web
versions,” which run directly in your browser (i.e., don't
require Java), but they are generally less capable, due to the
current limitations of NetLogoWeb.]
 The following are NetLogo 4.1 programs, which I’m gradually
converting to NetLogo 6.0, after which the older versions will
be removed. Most of them will run without change on NetLogo 6.0.
 The following are NetLogo 4.0 programs, which I’m gradually
converting to NetLogo 4.1, after which the older versions will
be removed.
 Fur — uses small and large
neighborhoods for activationinhibition 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
NetLogo.
 Vants — Langton’s
Vants (virtual ants)
 VantsLargeField
— Langton’s Vants on a large field (may take too much
memory)
 GeneralizedVants
— generalized vants, with a programmable rule
 Ants — simulation of
Resnick ants
 Firefly —
Camazine’s firefly synchronization model
 Firefliesoptmobile
— 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
 CBN Programs
You can run most of these as applets from the
Website for Flake’s textbook (see
Supplemental Material website) You can also download
sources and executables from there. Some of these are
already available in the experiments/CBN
subdirectory.
Online Resources
 Slides from the Spring
2018, Spring
2017, Spring
2016, Spring
2015, Spring
2014, Spring
2013, 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.
 Website
for Flake textbook.
 BarYam, Yaneer. Dynamics of Complex Systems.
Perseus, 1997,
available online in pdf format.

Interactive Models and Simulations keyed to BarYam book
(from NECSI)

Visualizing Complex Systems Science (from NECSI)
 Scientific institutes devoted to complex systems:
 Santa Fe Institute
 New England Complex Systems Institute
 WolframScience,
including online access to Wolfram’s A New Kind of Science
(NKS) and the Complex Systems
journal, focused on NKS.

NetLogo (a multiagent simulation language based on StarLogo)
 breve
(another multiagent simulational language)

Review of Gaussian (Normal) Distributions [pdf]
 A short introduction to Genetic Algorithms [pdf, ps]

PDP++ Software Homepage (Colorado). Neural net
software.

emergent package for computational cognitive neuroscience
(Colorado).
 Malasri
Hopfield Network Simulator (java applet)
 Golly Game of Life simulator: http://golly.sourceforge.net.
Youtube videos:
 Online demonstrations of genetic algorithms / evolutionary
algorithms:
 Online Demonstrations Illustrating Flocking Behavior
Return to MacLennan’s
home page
Send mail
to Bruce MacLennan / MacLennan@utk.edu
This page
is web.eecs.utk.edu/~mclennan/Classes/420527
Last updated: 20191205.