Table Of ContentStudies in Computational Intelligence 557
Taras Kowaliw
Nicolas Bredeche
René Doursat Editors
Growing
Adaptive
Machines
Combining Development and Learning
in Artificial Neural Networks
Studies in Computational Intelligence
Volume 557
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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Taras Kowaliw Nicolas Bredeche
•
René Doursat
Editors
Growing Adaptive
Machines
Combining Development and Learning
in Artificial Neural Networks
123
Editors
Taras Kowaliw René Doursat
Institutdes Systèmes Complexesde Paris School ofBiomedical Engineering
Île-de-France DrexelUniversity
CNRS Philadelphia, PA
Paris USA
France
Nicolas Bredeche
Instituteof IntelligentSystems
andRobotics
CNRSUMR 7222
Université Pierre etMarieCurie
Paris
France
ISSN 1860-949X ISSN 1860-9503 (electronic)
ISBN 978-3-642-55336-3 ISBN 978-3-642-55337-0 (eBook)
DOI 10.1007/978-3-642-55337-0
Springer Heidelberg NewYork Dordrecht London
LibraryofCongressControlNumber:2014941221
(cid:2)Springer-VerlagBerlinHeidelberg2014
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Preface
It is our conviction that the means of construction of artificial neural network
topologiesisanimportantareaofresearch.Thevalueofsuchmodelsispotentially
vast. From an applied viewpoint, identifying the appropriate design mechanisms
would make it possible to address scalability and complexity issues, which are
recognized as major concerns transversal to several communities. From a funda-
mentalviewpoint,theimportantfeaturesbehindcomplexnetworkdesignareyetto
be fully understood, even as partial knowledge becomes available, but scattered
within different communities.
Unfortunately,thisendeavourissplitamongdifferent,oftendisparatedomains.
Westartedaworkshopinthehopethattherewassignificantroomforsharingand
collaboration between these researchers. Our response to this perceived need was
togatherlike-motivatedresearchersintooneplacetopresentbothnovelworkand
summaries of research portfolio.
ItwasunderthisbannerthatweoriginallyorganizedtheDevLeaNNworkshop,
which took place at the Complex Systems Institute in Paris in October 2011. We
werefortunateenoughtoattractseveralnotablespeakersandco-authors:H.Berry,
C. Dimitrakakis, S. Doncieux, A. Dutech, A. Fontana, B. Girard, Y. Jin, M. Joa-
chimczak, J. F. Miller, J.-B. Mouret, C. Ollion, H. Paugam-Moisy, T. Pinville,
S.Rebecchi,P.Tonelli,T.Trappenberg,J.Triesch,Y.Sandamirskaya,M.Sebag,
B. Wróbel, andP. Zheng.The proceedings ofthe originalworkshop are available
online, at http://www.devleann.iscpif.fr. To capitalize on this grouping of
like-mindedresearchers,we movedtocreate anexpandedbook.Inmany(butnot
all) cases, the workshop contribution is subsumed by an expanded chapter in this
book.
In an effort to produce a more complete volume, we invited several additional
researchers to write chapters as well. These are: J. A. Bednar, Y. Bengio,
D.B.D’Ambrosio,J.Gauci,andK.O.Stanley.Theintroductionchapterwasalso
co-authored with us by S. Chevallier.
v
vi Preface
Our gratitude goes to our program committee, without whom the original
workshop would not have been possible: W. Banzhaf, H. Berry, S. Doncieux,
K. Downing, N. García-Pedrajas, Md. M. Islam, C. Linster, T. Menezes,
J. F. Miller, J.-M. Montanier, J.-B. Mouret, C. E. Myers, C. Ollion, T. Pinville,
S.Risi,D.Standage,P.Tonelli.OurfurtherthankstotheISC-PIF,theCNRS,and
toM.Kowaliwforhelpwiththeeditingprocess.Ourworkshopwasmadepossible
via a grant from the Région Île-de-France.
Enjoy!
Toronto, Canada, January 2014 Taras Kowaliw
Paris, France Nicolas Bredeche
Washington DC, USA René Doursat
Contents
1 Artificial Neurogenesis: An Introduction and Selective Review. . . . 1
Taras Kowaliw, Nicolas Bredeche, Sylvain Chevallier
and René Doursat
2 A Brief Introduction to Probabilistic Machine Learning
and Its Relation to Neuroscience. . . . . . . . . . . . . . . . . . . . . . . . . . 61
Thomas P. Trappenberg
3 Evolving Culture Versus Local Minima . . . . . . . . . . . . . . . . . . . . 109
Yoshua Bengio
4 Learning Sparse Features with an Auto-Associator. . . . . . . . . . . . 139
Sébastien Rebecchi, Hélène Paugam-Moisy and Michèle Sebag
5 HyperNEAT: The First Five Years. . . . . . . . . . . . . . . . . . . . . . . . 159
David B. D’Ambrosio, Jason Gauci and Kenneth O. Stanley
6 Using the Genetic Regulatory Evolving Artificial Networks
(GReaNs) Platform for Signal Processing, Animat Control,
and Artificial Multicellular Development. . . . . . . . . . . . . . . . . . . . 187
Borys Wróbel and Michał Joachimczak
7 Constructing Complex Systems Via Activity-Driven
Unsupervised Hebbian Self-Organization . . . . . . . . . . . . . . . . . . . 201
James A. Bednar
8 Neuro-Centric and Holocentric Approaches
to the Evolution of Developmental Neural Networks . . . . . . . . . . . 227
Julian F. Miller
9 Artificial Evolution of Plastic Neural Networks:
A Few Key Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Jean-Baptiste Mouret and Paul Tonelli
vii
Chapter 1
Artificial Neurogenesis: An Introduction
and Selective Review
TarasKowaliw,NicolasBredeche,SylvainChevallierandRenéDoursat
Abstract In this introduction and review—like in the book which follows—we
explore the hypothesis that adaptive growth is a means of producing brain-like
machines.Theemulationofneuraldevelopmentcanincorporatedesirablecharacter-
isticsofnaturalneuralsystemsintoengineereddesigns.Theintroductionbeginswith
areviewofneuraldevelopment andneuralmodels.Next,artificialdevelopment—
the use of a developmentally-inspired stage in engineering design—is introduced.
Severalstrategiesforperformingthis“meta-design”forartificialneuralsystemsare
reviewed.Thisworkisdividedintothreemaincategories:bio-inspiredrepresenta-
tions;developmentalsystems;andepigeneticsimulations.Severalspecificnetwork
biases and their benefits to neural network design are identified in these contexts.
Inparticular,severalrecentstudiesshowastrongsynergy,sometimesinterchange-
ability,betweendevelopmentalandepigeneticprocesses—atopicthathasremained
largelyunder-exploredintheliterature.
B
T.Kowaliw( )
InstitutdesSystèmesComplexes-ParisÎle-de-France,CNRS,Paris,France
e-mail:[email protected]
N.Bredeche
SorbonneUniversités,UPMCUniversityParis06,
UMR7222ISIR,F-75005Paris,France
e-mail:[email protected]
N.Bredeche
CNRS,UMR7222ISIR,F-75005Paris,France
S.Chevallier
VersaillesSystemsEngineeringLaboratory(LISV),UniversityofVersailles,
Velizy,France
e-mail:[email protected]
R.Doursat
SchoolofBiomedicalEngineering,DrexelUniversity,Philadelphia,USA
e-mail:[email protected]
T.Kowaliwetal.(eds.),GrowingAdaptiveMachines, 1
StudiesinComputationalIntelligence557,DOI:10.1007/978-3-642-55337-0_1,
©Springer-VerlagBerlinHeidelberg2014
2 T.Kowaliwetal.
This book is about growing adaptive machines. By this, we mean producing
programsthatgenerateneuralnetworks,which,inturn,arecapableoflearning.We
think this is possible because nature routinely does so. And despite the fact that
animals—thosemulticellularorganismsthatpossessanervoussystem—arestagger-
inglycomplex,theydevelopfromarelativelysmallsetofinstructions.Accordingly,
ourstrategyconcernsthesimulationofbiologicaldevelopmentasameansofgener-
ating,incontrasttodirectlydesigning,machinesthatcanlearn.Bycreatingabstrac-
tions of the growth process, we can explore their contribution to neural networks
fromtheviewpointofcomplexsystems,whichself-organizefromrelativelysimple
agents,andidentifymodelchoicesthatwillhelpusgeneratefunctionalanduseful
artefacts.Thispursuitishighlyinterdisciplinary:itisinspiredby,andoverlapswith,
computational neuroscience, systems biology, machine learning, complex systems
science,andartificiallife.
Throughgrowingadaptivemachines,ourambitionisalsotocontributetoaradical
reconception of engineering. We want to focus on the design of component-level
behaviourfromwhichhigher-levelintelligentmachinescanemerge.Thesuccessof
this“meta-design”[63]endeavourwillbemeasuredbyourcapacitytogeneratenew
learningmachines:machinesthatscale,machinesthatadapttonovelenvironments,
inshort,machinesthatexhibittherichnessweencounterinanimals,butpresently
eludesartificialsystems.
This chapter and the book that it introduces are centred around developmental
andlearningneuralnetworks.Itisatimelytopicconsideringtherecentresurgence
oftheneuralparadigmasamajorrepresentationformalisminmanytechnological
areas,suchascomputervision,signalprocessing,androboticcontrollers,together
withrapidprogressinthemodellingandapplicationsofcomplexsystemsandhighly
decentralizedprocesses.Researchersgenerallyestablishadistinctionbetweenstruc-
tural design, focusing on the network topology, and synaptic design, defining the
weightsoftheconnectionsinanetwork[278].Thisbookexamineshowonecould
create a biologically inspired network structure capable of synaptic training, and
blend synaptic and structural processes to let functionally suitable networks self-
organize.Insodoing,theaimistorecreatesomeofthenaturalphenomenathathave
inspiredthisapproach.
Thepresentchapterisorganizedasfollows:itbeginswithabroaddescriptionof
neuralsystemsandanoverviewofexistingmodelsincomputationalneuroscience.
Thisisfollowedbyadiscussionofartificialdevelopmentandartificialneurogenesis
in general terms, with the objective of presenting an introduction and motivation
for both. Finally, three high-level strategies related to artificial neurogenesis are
explored:first,bio-inspiredrepresentations,wherenetworkorganizationisinspired
byempiricalstudiesandusedasatemplatefornetworkdesign;then,developmental
simulation,wherenetworksgrowbyaprocesssimulatingbiologicalembryogenesis;
finally,epigeneticsimulation,wherelearningisusedasthemainstepinthedesign
ofthenetwork.Thecontributionsgatheredinthisbookarewrittenbyexpertsinthe
fieldandcontainstate-of-the-artdescriptionsofthesedomains,includingreviewsof
originalresearch. We summarize their workhere and place itinthe context ofthe
meta-designofdevelopmentallearningmachines.
