guest editorial: special issue on computational modeling of neural and brain development

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, VOL. 3, NO. 4, DECEMBER 2011 273 Guest Editorial Special Issue on Computational Modeling of Neural and Brain Development Much work on mental development has adopted a symbolic approach. Whereas these methods have achieved considerable success, we believe that symbolic methods correspond to an early stage in our understanding of how the intelligence takes place in natural systems. By contrast, neural development-based approaches, where the components of a network emerge from interactions with the external environment, have a potential to resolve the well-known limitations of symbolic methods, such as the high brittleness because: 1) a human cannot fully predict an open and rich real world; and 2) the system cannot go beyond handcrafted representations. Neural and brain development include mitosis, cell differen- tiation, neuron migration, axon and dendrite growth, guidance, spine growth, synapse maintenance, role determination, neuro- modulation, and various levels of plasticity. These processes are shaped by underpinning principles of genome functions and ac- tivities. These principles are essential for the brain to display ca- pabilities in perception, cognition, behaviors, and emotion. Re- search efforts devoted to the computational modeling of neural and system development were already made at the beginning of 1990s. Recently, new findings are quickly growing in neuro- science, psychology, genetics, and systems biology, which pro- vides new impetus for research on understanding neural and brain development using computational approaches. This Special Issue aims to attract research work that reflects the state-of-the-art in computational modeling of neural and brain development, including genetic, biochemical, and cellular mechanisms in neural development; computational model of neural, circuit, system, and brain plasticity; neuromodulation and its roles; cellular mechanisms for perceptual, cognitive, be- havioral, and emotional development. In response to the call for papers, we received 12 full papers. All the received papers have undergone a rigorous review process like regular submissions to the TRANSACTIONS, including the principle of conflict-of-in- terest. Based on the review results, four papers addressing var- ious aspects of neural and brain development have been included in the Special Issue. Li et al.’s paper, “A Model of Neuronal Intrinsic Plasticity,” suggests that the probability distribution of the neuronal firing rates can be better described by a Weibull distribution rather than an exponential distribution. With the Weibull distribution, the proposed new model for intrinsic plasticity is believed to be able to both minimize the firing rate and maximize the sensitivity. By using the Weibull distribution and based on an information theoretical approach, a learning rule for neuronal intrinsic plasticity has been developed. The proposed model Digital Object Identifier 10.1109/TAMD.2011.2172729 is verified by numerical simulations. It is also indicated that the Weibull distribution is more flexible in the sense that by adjusting its parameters, it can represent or approximate many other probability distributions, including the exponential, Rayleigh, and Gaussian distributions. Glaser and Joublin’s paper, “Firing Rate Homeostasis for Dy- namic Neural Field Formation,” addresses the homeostasis of the firing rate of individual neurons. They implemented a bio- logically inspired dynamic self-regulation based on the self-or- ganization map network. In their model, average activity of each neuron is incrementally computed. This average activity is com- pared with a target rate for the neuron. The result of comparison determines the release of the simulated neurotrophin brain-de- rived neurotrophic factor (BDNF). The synaptic weight updated is related to the two versions of BDNF, excitatory and inhibitory, in a reciprocal way. Furthermore, they aim to develop topology preserving mapping. Their model uses fully plastic within field connections to further decouple learning from topological con- straints. Specifically, they define the connection weight between two units to be proportional to the similarity between the recep- tive fields. The model has been tested using simulated data as well as continuous speech data. The paper “Probabilistic Computational Neurogenetic Mod- eling: From Cognitive Systems to Alzheimer’s Disease,” by Kasabov, Schliebs, and Kojima proposes a research framework for building probabilistic computational neurogenetic models (pCNGM) and their use for building cognitive systems, and modeling brain functions and diseases. The framework contains molecular level models, a more abstract dynamic model of a protein regulatory network (PRN), and a probabilistic spiking neural network (pSNN) model, all linked together. Genes and proteins from the PRN control probability parameters of the pSNN. The overall spatio–temporal pattern of spiking activity of the pSNN is interpreted as the highest level state of the pCNGM. The paper demonstrates that this framework can be used for modeling both artificial cognitive systems and brain processes. An exemplar case study on Alzheimer’s Disease is presented and its PRN discussed in detail. In Andreae’s paper “A Multiple Context Brain for Experi- ments with Robot Consciousness,” a PURR- ( for short) is proposed to model the consciousness of human-like brain, which is the extension of the author’s previous subcon- scious PP model. In , a new level of consciousness is added, such as a “trail memory” inspired by the global workspace of Baars, and a “belief memory” to test the “higher order thought” theory of Rosenthal and Johnson-Larid’s conscious reasoning. is designed to give consciousness to the subconscious PP, but higher order thoughts and conscious reasoning prove to be elusive. PP and are designed to be implemented in parallel 1943-0604/$26.00 © 2011 IEEE

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Page 1: Guest Editorial: Special Issue on Computational Modeling of Neural and Brain Development

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, VOL. 3, NO. 4, DECEMBER 2011 273

Guest EditorialSpecial Issue on Computational Modeling of

Neural and Brain DevelopmentMuch work on mental development has adopted a symbolic

approach. Whereas these methods have achieved considerablesuccess, we believe that symbolic methods correspond to anearly stage in our understanding of how the intelligence takesplace in natural systems. By contrast, neural development-basedapproaches, where the components of a network emerge frominteractions with the external environment, have a potential toresolve the well-known limitations of symbolic methods, suchas the high brittleness because: 1) a human cannot fully predictan open and rich real world; and 2) the system cannot go beyondhandcrafted representations.

Neural and brain development include mitosis, cell differen-tiation, neuron migration, axon and dendrite growth, guidance,spine growth, synapse maintenance, role determination, neuro-modulation, and various levels of plasticity. These processes areshaped by underpinning principles of genome functions and ac-tivities. These principles are essential for the brain to display ca-pabilities in perception, cognition, behaviors, and emotion. Re-search efforts devoted to the computational modeling of neuraland system development were already made at the beginningof 1990s. Recently, new findings are quickly growing in neuro-science, psychology, genetics, and systems biology, which pro-vides new impetus for research on understanding neural andbrain development using computational approaches.

This Special Issue aims to attract research work that reflectsthe state-of-the-art in computational modeling of neural andbrain development, including genetic, biochemical, and cellularmechanisms in neural development; computational model ofneural, circuit, system, and brain plasticity; neuromodulationand its roles; cellular mechanisms for perceptual, cognitive, be-havioral, and emotional development. In response to the call forpapers, we received 12 full papers. All the received papers haveundergone a rigorous review process like regular submissionsto the TRANSACTIONS, including the principle of conflict-of-in-terest. Based on the review results, four papers addressing var-ious aspects of neural and brain development have been includedin the Special Issue.

Li et al.’s paper, “A Model of Neuronal Intrinsic Plasticity,”suggests that the probability distribution of the neuronal firingrates can be better described by a Weibull distribution ratherthan an exponential distribution. With the Weibull distribution,the proposed new model for intrinsic plasticity is believedto be able to both minimize the firing rate and maximize thesensitivity. By using the Weibull distribution and based on aninformation theoretical approach, a learning rule for neuronalintrinsic plasticity has been developed. The proposed model

Digital Object Identifier 10.1109/TAMD.2011.2172729

is verified by numerical simulations. It is also indicated thatthe Weibull distribution is more flexible in the sense thatby adjusting its parameters, it can represent or approximatemany other probability distributions, including the exponential,Rayleigh, and Gaussian distributions.

Glaser and Joublin’s paper, “Firing Rate Homeostasis for Dy-namic Neural Field Formation,” addresses the homeostasis ofthe firing rate of individual neurons. They implemented a bio-logically inspired dynamic self-regulation based on the self-or-ganization map network. In their model, average activity of eachneuron is incrementally computed. This average activity is com-pared with a target rate for the neuron. The result of comparisondetermines the release of the simulated neurotrophin brain-de-rived neurotrophic factor (BDNF). The synaptic weight updatedis related to the two versions of BDNF, excitatory and inhibitory,in a reciprocal way. Furthermore, they aim to develop topologypreserving mapping. Their model uses fully plastic within fieldconnections to further decouple learning from topological con-straints. Specifically, they define the connection weight betweentwo units to be proportional to the similarity between the recep-tive fields. The model has been tested using simulated data aswell as continuous speech data.

The paper “Probabilistic Computational Neurogenetic Mod-eling: From Cognitive Systems to Alzheimer’s Disease,” byKasabov, Schliebs, and Kojima proposes a research frameworkfor building probabilistic computational neurogenetic models(pCNGM) and their use for building cognitive systems, andmodeling brain functions and diseases. The framework containsmolecular level models, a more abstract dynamic model of aprotein regulatory network (PRN), and a probabilistic spikingneural network (pSNN) model, all linked together. Genes andproteins from the PRN control probability parameters of thepSNN. The overall spatio–temporal pattern of spiking activityof the pSNN is interpreted as the highest level state of thepCNGM. The paper demonstrates that this framework can beused for modeling both artificial cognitive systems and brainprocesses. An exemplar case study on Alzheimer’s Disease ispresented and its PRN discussed in detail.

In Andreae’s paper “A Multiple Context Brain for Experi-ments with Robot Consciousness,” a PURR- ( forshort) is proposed to model the consciousness of human-likebrain, which is the extension of the author’s previous subcon-scious PP model. In , a new level of consciousness is added,such as a “trail memory” inspired by the global workspace ofBaars, and a “belief memory” to test the “higher order thought”theory of Rosenthal and Johnson-Larid’s conscious reasoning.

is designed to give consciousness to the subconscious PP,but higher order thoughts and conscious reasoning prove to beelusive. PP and are designed to be implemented in parallel

1943-0604/$26.00 © 2011 IEEE

Page 2: Guest Editorial: Special Issue on Computational Modeling of Neural and Brain Development

274 IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, VOL. 3, NO. 4, DECEMBER 2011

hardware and embodied in the head of a robot moving in the realworld. To provide concrete examples of structures and interac-tions, a squash-pop microworld simulation with two robots andone food source is implemented to evaluate the proposedmodel.

ACKNOWLEDGMENT

The Guest Editors would like to thank the Editor-in-Chieffor working with us throughout the whole process. We wouldalso like to thank the authors for submitting their papers andthe reviewers for their insightful reviews. We hope that youfind these papers interesting and inspiring. We encourage moreautonomous mental development (AMD) researchers to studycomputational modeling of neural and brain development.

YAOCHU JIN, Guest EditorNature Inspired Computing and Engineering (NICE)University of SurreyGuildford, GU2 7XH U.K.E-mail: [email protected]

YAN MENG, Guest EditorDepartment of Electrical and Computer EngineeringStevens Institute of TechnologyHoboken, NJ 07030 USAE-mail: [email protected]

JUYANG WENG, Guest EditorComputer Science, Neuroscience, and Cognitive

ScienceMichigan State UniversityEast Lansing, MI 48824 USAE-mail: [email protected]

NIKOLA KASABOV, Guest EditorKEDRIAuckland University of TechnologyAuckland, 1010 New Zealandand the Institute for NeuroinformaticsETH and University of ZurichZurich, 8057 SwitzerlandE-mail: [email protected]

Yaochu Jin (M’98–SM’02) received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang Univer-sity, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr-UniversityBochum, Germany, in 2001.

He is currently a Professor of Computational Intelligence and Head of the Nature Inspired Com-puting and Engineering (NICE) Group in the Department of Computing, University of Surrey, U.K.His research interests include understanding evolution, learning and development in biology andbio-inspired approaches to solving engineering problems.

He is an Associate Editor of seven international journals, including three IEEE Transactions.He has delivered over ten Keynote speeches at international conferences on multiobjective ma-chine learning, computational modeling of neural development, morphogenetic robotics, and evo-lutionary aerodynamic design optimization. He is a Fellow of BCS.

Yan Meng (M’03) received the B.S. and M.Sc. degrees from Xian Jiaotong University, Xi’an,China, in 1991 and 1994, respectively, and the Ph.D. degree from Florida Atlantic University, BocaRaton, FL, in 2000.

She is currently a Faculty Member in the Department of Electrical and Computer Engineering,Stevens Institute of Technology, Hoboken, NJ. Her research interests include cognitive architec-ture for intelligent robotic systems, bio-inspired self-organizing robotic systems, neurocognitiveapproaches for autonomous machine learning, real-time embedded systems, and bioinformatics.She has published over 85 journal and conference papers in these areas.

Page 3: Guest Editorial: Special Issue on Computational Modeling of Neural and Brain Development

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, VOL. 3, NO. 4, DECEMBER 2011 275

Juyang Weng (S’85–M’88–SM’05–F’09) received the B.Sc. degree in computer science fromFudan University, Shanghai, China, in 1982, and the M.Sc. and Ph.D. degrees in computer sciencefrom the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively.

He is currently a Professor of Computer Science and Engineering at Michigan State University,East Lansing. He is also a faculty member of the Cognitive Science Program and the NeuroscienceProgram at Michigan State University. Since the work of Cresceptron (ICCV 1993), he expandedhis research interests in biologically inspired systems, especially the autonomous development ofa variety of mental capabilities by robots and animals, including perception, cognition, behaviors,motivation, and abstract reasoning skills. He has published over 200 research articles on related sub-jects, including task muddiness, intelligence metrics, mental architectures, vision, audition, touch,attention, recognition, autonomous navigation, and other emergent behaviors.

Dr. Weng is an Editor-in-Chief of the I3 and an Associate Editor of the IEEE TRANSACTIONS ON

AUTONOMOUS MENTAL DEVELOPMENT, as well as a member of the Executive Board of Interna-tional Neural Network Society. He was a Program Chairman of the NSF/ DARPA fundedWorkshop on Development and Learning2000 (1st ICDL), the Chairman of the Governing Board of the International Conferences on Development and Learning (ICDL)(20052007), Chairman of the Autonomous Mental Development Technical Committee of the IEEE Computational IntelligenceSociety (20042005), a Program Chairman of 2nd ICDL, a General Chairman of 7th ICDL (2008) and 8th ICDL (2009), an Asso-ciate Editor of the IEEE TRANSACTIONS ON PATTERN RECOGNITION AND MACHINE INTELLIGENCE, and an Associate Editor of theIEEE TRANSACTIONS ON IMAGE PROCESSING. He and his co-workers developed SAIL and Dav robots as research platforms forautonomous development.

Nikola Kasabov (M’93–SM’98–F’10) received the M.Sc. degree in computing and electrical en-gineering, and the Ph.D. degree in mathematical sciences from the Technical University of Sofia,Bulgaria, in 1971 and 1975, respectively.

He is currently the Director and Founder of the Knowledge Engineering and Discovery ResearchInstitute (KEDRI), and a Professor of Knowledge Engineering at the School of Computing andMathematical Sciences at the Auckland University of Technology, Auckland, New Zealand. Priorto that, he worked as a professor at the University of Otago, a Senior Lecturer at the University ofEssex, and an Associate Professor in Sofia. He has published more than 420 papers, books, andpatents in the areas of information science, computational intelligence, neural networks, bioinfor-matics, and neuroinformatics.

Dr. Kasabov is a Past President of the International Neural Network Society (INNS) and theAsia Pacific Neural Network Assembly (APNNA). He is a Distinguished IEEE CIS Lecturer(20112013), an EU FP7 Marie Curie Fellow (20112012) in the Institute for Neuroinformatics of

the University of Zurich and ETH, and a Guest professor at the Shanghai Jiao Tong University (20102013. He has served as achair and a program committee member of numerous IEEE, ICONIP, ANNES, and other international conferences.