1. In search of a cyberspace … to launch 1
biologically-inspired advanced computing strategies:
a digital ecology solution
Dr. Perambur S. Neelakanta, Ph.D., C. Eng., Fellow IEE
Professor
Department of Electrical Engineering
College of Engineering and Computer Science
Florida Atlantic University
Boca Raton, Florida 33431, USA
neelakan@fau.edu
Invited Lecture
International Conference on Advanced Computing (ICAC 2009),
August 7-8, 2009, Tiruchirappalli, Tamil Nadu, India
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Biologically-inspired computing (BIC)…?
Simply, known as bio-inspired computing (or just bio-computing),
BIC denotes…
“a field of study that loosely knits together subfields
related to the topics of connectionism, social behavior and
emergence.
It is often closely related to the field of artificial
intelligence, as many of its pursuits can be linked to machine
learning.
It relies heavily on the fields of biology, computer
science and mathematics…”.
In nut-shell, BIC is the use of computers to model nature, and
simultaneously the study of nature to improve the usage of
computers. It is, therefore a major subset of natural computation.
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3. In search of a cyberspace … 3
…to launch biologically-inspired advanced computing
strategies….
Whether the strategies of BIC comes within the purview of
information technology (IT)-oriented considerations is still
unclear and remains as an open-question.
This paper heuristically searches for a cyberspace wherein
BIC efforts can be viewed cohesively in the broader sense of
IT-paradigms.
Hence, attempted here is an exploration to
cast comprehensively the universe of BIC in
the domain of so-called…
“digital ecology” (DE)
Now what is “digital ecology”? 3

4. 4
Now what is “digital ecology”?
Digital ecology (DE) is a neoteric terminology mostly
applied to the evolution of social and civic ecosystem
commensurate with modern IT perspectives
Its usage in modern context includes the plethora of (i)
entertainment media ecology, (ii) the entirety of computing
ambient and (iii) the environment of communication
networks.
In each of this gamut, the transfer of information (or
informatics) negotiates a sizable cardinality of
stochastically interacting stochastically interacting subsets
that structuralize a complex open-source network and
computational environment.
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Now what is “digital ecology”? … Continued
In short, DE refers to an environment, which is:
- open in visibly portraying the interactions involved;
- loosely-coupled in mediating the open relationships
between species;
- domain-clustered in creating a field of balanced
common interest;
- demand-driven in conglomerating the species as
interest groups;
- self-organizing in autonomous decision-making; and,
- agent-based in rendering an ambient of synergism
between human and machines where each agent
participates proactively in the computational endeavors
as well as in the information transfers akin to the
species of biological ecosystem.
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“Digital ecology” …a cyberspace to launch
biologically-inspired advanced computing strategies
Digital ecology enables a unified presentation of
computational tools and algorithmic endeavors modern
and advanced computing schemes) in an IT-specific
domain. So attempted here in an ambient of BIC efforts
towards…
… constructing a DE platform to support
BIC concepts
As an illustrative example, the strategy of artificial
neural networks (ANN) mapped in terms of relevant
ontological norms of digital ecology is presented.
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Biologically-inspired computing (BIC)…More
BIC bears the perspectives of cybernetics in the
computational efforts involving …
simulated annealing
artificial neural networks
genetic algorithms
DNA and molecular computing
biological ecology etc.
Thus, the field of BIC is highly multidisciplinary,
attracting a host of disciplines…
- …computer science, molecular biology, genetics,
engineering, mathematics, physics, chemistry
and others.
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8. Biologically-inspired computing (BIC)……potential 8
applications in:
DNA computation
nanofabrication
storage devices
sensing
healthcare
basic scientific research – for example …
…providing biologists with an IT-oriented
paradigm to look at how cells “compute” or process the
information
…helping computer scientists and engineers to
construct algorithms based on natural systems, such as
evolutionary and genetic algorithms
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Biologically-inspired computing (BIC)…
BIC… its scope
Enabling new themes of computing technologies
and fresh areas of computer science using biology
or biological processes as metaphor/inspiration
Expanding information science concepts and
tools to explore biology from a different theoretical
perspective.
BIC as such, however, does not include in its scope
the framework of, (i) the general use of computers;
(ii) the strategies of computational analyses,
and/or (iii) data management in biology - for
example, bioinformatics or computational biology.
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Biologically-inspired computing (BIC)…
… BIC and its cousins: Areas of emphasis
Genetic algorithms (GAs) ↔ Follows natural evolution with
the rules of selection, recombination, reproduction, mutation
and more recently transposition. Such simple rules of evolution
in complex organisms are observed and adopted in GAs
constituting BIC approach.
Artificial Intelligence (AI) ↔ Traditional AI is the intelligence
of machines towards the design of intelligent agents.
The way in which BIC differs from traditional AI is in how it
takes a more evolutionary approach to learning, as opposed to
what could be described as 'creationist' methods used in
traditional AI. In this perspective AI inclines towards BIC.
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BIC and its cousins: Areas of emphasis… continued
Biodegradability prediction ↔ Accurate sequence
details and genetic information vis-à-vis biodegradation are
essential for assessing molecular basis of enzyme specificity,
their catalytic mechanism, the evolutionary origin of
related metabolism and proliferation of such activities in
the environment.
(Although some basic formalization toward useful
tools as a predictor of chemical/biodegradability is
feasible, the absence of information at the sequence level
of proteins etc. are imminently required for systematic
studies of biodegradation. This is facilitated via
biocomputing).
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BIC and its cousins: Areas of emphasis… continued
Cellular automata ↔ Cellular automaton is a discrete
model of a regular grid of cells, each in one of a finite
number of states.
Relevant evolutionary computation programs with cellular
arrays in decentralized platforms (where the information
processing occurs in the form of global and local pattern
dynamics) lead to emergent computation (expressed in
terms of GAs) and adopted to evolve patterns in cellular
automata in the perspectives of BIC.
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BIC and its cousins: Areas of emphasis… continued
Emergent systems ↔ The way complex systems and
patterns arise out of a multiplicity of relatively simple
interactions as in biological systems is specified by
“emergence”.
It has been the holy grail of BIC. Emergence is
something like a macro phenomenon that appears as a
by-product of a (generally but not always large)
collection of micro phenomena.
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BIC and its cousins: Areas of emphasis… continued
Neural networks ↔ Biological neural networks are made up of
real biological neurons that are connected or functionally related
in the peripheral nervous system or the central nervous system.
Artificial neural networks (ANNs) are composed of
simulated neuron units made “in the image of real neurons”. By
interconnecting “artificial neurons” – a programming strategy is
set up that constructs a massively parallel connectivity, mimicing
the biological neurons.
ANN with its interconnected structure of artificial
neuron uses a paradigm of mathematical or computational model
for information processing based on a connectionist approach to
computation adaptively to changes in external or internal
information via biological-inspiration.
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BIC and its cousins: Areas of emphasis… continued
Artificial life ↔ Commonly known as Alife or alife, it
depicts a field of study and an associated art form
which examine systems related to life, its processes, and
its evolution through simulations using computer
models, robotics, and biochemistry.
There are three major versions of alife, based on their
approaches: soft- from software; hard- from hardware;
and wet- from biochemistry. Artificial life imitates
traditional biology in recreating biological phenomena.
Essentially, the term "artificial life" is often used to
specifically refer to soft alife.
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BIC and its cousins: Areas of emphasis… continued
Artificial immune systems (AIS) ↔ Abstracting and
mapping the structure and function of an immune system
to a computational set of frameworks so as to investigate
the application of such systems towards solving
computational problems with the aid of mathematics,
engineering, and information technology.
AIS is a sub-field of computational intelligence, BIC,
and natural computation, with a focus on machine
learning. It can be said to belong the broader field of AI.
Further, AIS are adaptive systems, inspired by theoretical
immunology and observed immune functions, principles
and models, which are applied to problem solving.
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BIC and its cousins: Areas of emphasis… continued
Rendering (computer graphics) ↔ a process of generating
an image from a model (description of 3D objects in a
strictly defined language or data structure) using
computer programs.
It contains features of geometry, viewpoint, texture,
lighting, and shading information in digital image or a
raster graphics image format.
The term rendering in computing context is an
analogy of an "artist's rendering" of a scene. (In biological
context, rendering simply refers to patterning and
rendering of animal skins, bird feathers, mollusk shells
and bacterial colonies)
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BIC and its cousins: Areas of emphasis… continued
Lindenmeyer systems ↔ Computing self-organization in
the context of environmentally sensitive growth and/or
development modeling behavior and visualization of cells
of plants/plant structures:
- Mathematical, spatial models that treat plant geometry
as a continuum or as discrete components in space.
- Developmental models that describe form as a result of
growth in terms of growth influencing variables
- Simulations produce numerical output, which can be
complemented by rendered images and animations for the
purpose of easy comprehension
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BIC and its cousins: Areas of emphasis… continued
Communication networks and protocols ↔ Analogy
between viral dynamics in humans and in computers is
useful in assessing infectious disease epidemiology on
human social networks versus communication in
wireless networks
Epidemiology as a metaphor may hold insights into
communication networks.
New paradigms of mathematics and methodologies
sought towards linking epidemiology and the spread of
disease are generalized biological-inspirations seen
toward modeling modern communication systems.
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BIC and its cousins: Areas of emphasis… continued
Membrane computers ↔ The membrane computing is
an effort to replicate organic structures of the brain
and the intra-membrane molecular processes in the
living cells onto silicone.
This is to create indeterminate outcome machines
that are capable of learning through external stimuli.
Such membrane computers will be a interesting
technology when it is finally developed, say in creating
artificial brains and teaching machines… a dream
sought in BIC.
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BIC and its cousins: Areas of emphasis… continued
Excitable media ↔ An excitable medium is a nonlinear
dynamical system that has the capacity to propagate a wave of
some description, experiencing an elapsed time (refractory
time). A forest is an example of an excitable medium: That is,
when a wildfire burns through the forest, no fire can return to
a burnt spot until the vegetation has gone through its
refractory period and re-grown. BIC implications are related
to…
Pathological activities in the heart and brain can also be
modeled as excitable media.
In cellular automata the state of a particular cell in the
next time step depends on the state of the cells around it--its
neighbors-at the current time.
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BIC and its cousins: Areas of emphasis… continued
Sensor networks ↔ Sensor networks are a sensing,
computing and communication infrastructure that
allows to instrument, observe, and respond to
phenomena in the natural environment, and in the
physical as well as cyber infrastructure.
Akin to biological systems that present remarkable
adaptation, reliability, and robustness in various
environments, even under hostility (in a distributed and
self-organized way), they provide useful resources for
designing the dynamical and adaptive routing schemes
of wireless mobile sensor networks.
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BIC and its cousins: Areas of emphasis… continued
DNA computing ↔ a computing strategy that uses
interdisciplinary aspects of DNA, biochemistry and
molecular biology, instead of the traditional silicon-based
computer technologies. It is a molecular computing stategy
similar to parallel computing and employs many different
molecules of DNA to try many different information
processing at once. Mostly, DNA computers are faster and
smaller than any other computer built so far. However,
unlike quantum computing, in DNA machines to solve
extremely large EXPSPACE problems, the amount of DNA
required is too large to be practical.
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BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE?
Biologically-inspired computing will be “wonderful tools,
(and) will eventually lead the way to a “molecular
revolution,” which ultimately will have a very dramatic
effect on the world”. As such biocomputing, in general has
the potential to be a very powerful tool.
BIC shouldering the marvels of computation per
se is not the traditional “computing with silicon-chips”, but
in essence, it. It relies on information-science (technology?)
and borrows the metaphors from biological sciences.
The query that lingers is whether the various
avenues of BIC can be comprehended in a unified
cyberspace. If so, how?
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BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
In modern perspective, in sheltering the BIC within the
scope of IT-oriented considerations is still unclear and
remains as an open-question.
Suppose BIC-related computational tools and algorithmic
endeavors are to be viewed in an IT-specific cyberspace.
It is then necessary to seek a platform that permits a
cohesive activity of a complex system where biological
evolutionary principles are invoked in terms of interacting
species having self-organizing features. Further overlaid
thereon are feasible aspects of informatics and paradigms
of computation.
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BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
Can the underlying abstract of a unified cyberspace of BIC
be specified in the so-called digital ecology (DE) platform
towards a compatible solution?
DE is “the medley of digital code and
environmentalism” that prescribes information ecosystems
constituted by information flows being processed through
various mediating species across biological ecology. In this
perspective, considering the intersecting aspects of a
complex system and ecological prescriptions, models of
BIC can be projected in the realm of digital ecosystem
ontology.
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BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
Digital ecosystems have been conceived in “the image of” complex
biological ecology expressed in terms of "digital environment"
ontology and is populated by "digital species" that mediate
massive information exchange.
Compared with natural ecosystems where species may
follow adaptation to local conditions, in digital ecosystem, new
digital species continuously emerge and they help cleanse the
ecosystem (for example supplanting older scheme of computation
with an advanced one).
Digital ecosystems thus capture the essence of classical,
complex ecological environment in nature, where organisms
cohesively constitute a dynamic, self-organizing and interrelated
complex ecosystem conserving and utilizing the environment of its
resources.
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BIC: A COMPLEX SYSTEM THAT FOLLOWS
A DIGITAL ECOSYSTEM ONTOLOGY...
… a possible suite for modeling the complex system
profile of BIC is to apply DE considerations identified
in terms of certain DE ontology nomenclature:
{Species} ⇔ {Domain, Task, Profit, Rule, Role,
Supplier, Requester, Available
Service, Requested Service}
{Environment} ⇔ {Technology, Service, (Species),
Open-environment, Loosely-coupled
environment, Demand-driven
environment, Domain-clustered
environment}
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BIC: SPELT IN THE ONTOLOGY OF DIGITAL
ECOLOGY – AN EXAMPLE…ANN
{Species} ⇒ {Domain, Task, Profit, Rule, Role,
Supplier, Requester, Available
Service, Requested Service}
{Environment} ⇒ {Open, loosely-coupled, demand-
driven; domain-clustered}
⇑ ⇓
{Interacting neurons, layered ANN architecture, massively
parallel computation, output/goal-realization, nonlinear
processing of collective information, supervised learning;
output validation via teacher value, input ambient, user
(programmer), convergence of the output against learned
pattern, testing an input set against learned pattern}
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BIC: SPELT IN THE ONTOLOGY OF DIGITAL
ECOLOGY – AN ANN EXAMPLE…continued
Teacher
Input Hidden
Input
layer layers
Weights Weights Ouput Ti
zi layer
+
Σ –
Inputs Oi
yi = f(xi) Σ
Oi = KΣzi
A
neuronal Weight vector εi = (Oi, Ti)
unit adjustments Error
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31. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 31
– AN ANN EXAMPLE…continued
(A) Subfunction PeudoCode I on:
DEFINE_(ANN-DE)_SPECIES & ENVIRONMENT–ONTOLOGY: Initialize
⇒ FOR Complex ANN system: Neurons/neuronal units
CALL: DEFINE_ENVIRONMENT: ANN
DEFINE_SPECIES: Neuronal units ⇒
comesFrom -domain ANN architecture
DEFINE_DOMAIN: ⇒ common field for all species
DEFINE_TASK carriesOut goal-oriented tasks
Goal: converged ANN output
DEFINE_PROFIT relatesTo task
- computational advantage
isDrivenBy species: neurons
DEFINE_RULE:-follows nonlinear norms regulating
species collectively
DEFINE_ROLE- role of interaction with other
Species (neurons) defineBy weight-modification,
inter-play of input data at the hidden layer(s)
CALL: DEFINE_SUPPLIER
CALL: DEFINE_REQUESTER
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32. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 32
– AN ANN EXAMPLE…continued
(B) Subfunction Code II on: ENVIRONMENT ontology -
Initialize:
Inputs: Training and prediction sets:
DEFINE_DIGITAL_ECOSYSTEM: ANN
DEFINE_ENVIRONMENT
⇒ architecture items of SPECIES
DEFINE_TECHNOLOGY of the Environment isSupportedBy INPUTS
and Teacher values
Connectivity isProvidedBy SPECIES
GOTO: SPECIES
DEFINE_SERVICES
Error feedback –backpropagation etc.
Weighting is rendered on
SPECIES/Interconnected
DEFINE_ENVIRONMENT set:{open, demand-driven, agent-based,
self-
organizing, domain-clustered, loosely-coupled}-ANN
architecture
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33. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 33
– AN ANN EXAMPLE…continued
Computation of: ANN Output
Inputs to: { Species and Environment}:
←DOMAIN data set {details on neurons, layers, logistic function,
momentum function, learning
coefficient}
← ENVIRONMENT data set {Training data set to visible neurons,
teacher values}
← TASK data set {Defining error, type of feedback etc.}
← RULE data set {Stop criterion on iterations, tuning the
weighting coefficients}
← ROLE data set {Adjusting the nonlinearity, momentum and
learning towards convergence}
← REQUESTER data set {Input data to visible neurons, teacher set}}
← SUPPLIER data set {ANN user}
Compute I: Related subfunctions towards output Oi(t)
← REQUESTER observation at the output node
Compute II: IF computed error is too high,
← THEN do iteration
← OR ELSE, GOTO Compute I
END
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34. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 34
– AN ANN EXAMPLE…continued
Subfunction Codes on: SUPPLIER and REQUESTER
Subfunction Code IIA Subfunction Code IIB on:
on: REQUESTER
SUPPLIER suite of suite of SPECIES ontology
SPECIES ontology DEFINE_ROLE
Convergence toward
DEFINE_ROLE objective function
DEFINE_SUPPLIER DEFINE_REQUESTER
-ANN user ⇒ ANN output
DEFINE_REQUESTED_
DEFINE_AVAILABLE_ SERVICE
SERVICE ⇒ Convergence
- ANN capability towards the
goal sought
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35. 35A
REFERENCES
[1] N. Forbes, Biologically inspired computing, Computing in Science and Engineering,
November/December 2000, vol. 2(6), 84-87
[2] H. Boley and E. Chang, “Digital ecosystem: Principles and semantics,” in 2007 Inaugural
IEEE International Conference in Digital Ecosystems and Technologies (IEEE DEST 2007),
2007, 1-4244-047003/07.
[3] H. Dong, F. K. Hussain, and E. Chong, “Ontology-based digital ecosystem conceptual
representation,” in Proceedings of the Third International Conference on Automatic and
Autonomous Systems (ICAS’07), 2007, 0-7695-2859-5/07
[4] P. S. Neelakanta and R. C. Tourinho, Modeling an It-centric complex system via digital
ecology concepts, Presented in Third IEEE International Conference on Digital Ecosystems
and Technologies (IEEE-DEST 2009), Istanbul, Turkey, 31 May 2009 – 3 June 2009)
[5] G. W. Flake: The Computational Beauty of Nature, MIT Press. Boston, MA: 2000
[6] P.S. Neelakanta and D. De Groff, Neural Network Modeling: Statistical Mechanics and
Cybernetic Perspectives, CRC Press, Boca Raton, FL, 1994.
[7] P.S. Neelakanta, “Dynamics of neural learning in the information theoretic plane,” Chapter
5, Information-Theoretic Aspects of Neural Networks (Editor: P.S. Neelakanta), CRC Press,
Boca Raton, FL, 1999.
[8] L. M. Adleman, Computing with DNA, Scientific American, August 1998, 54-61
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36. 35B
In search of a cyberspace … to launch BIC…
Conclusions
This study attempts to portray biologically-motivated
computing considerations…
… in the framework of a complex digital ecosystem.
… the ANN is chosen as an example and characterized in
the domain of interest.
… Relevant details on ANN describe the relational
aspects of Species and Environment vis-à-vis the BIC
in terms of the ontological details of [3].
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THANK YOU!!!