Brain inspired cognitive systems (BICS 2004)
2
Neurocomputing 69 (2006) 1825–1826 Guest Editorial Brain inspired cognitive systems (BICS 2004) This Special Issue is based on selected, expanded and significantly revised versions of papers presented at the first International Conference on Brain Inspired Cognitive Systems (BICS 2004) held at Stirling, 29 August–1 September 2004. The aim of BICS 2004 was to bring together leading scientists and engineers who use analytic, syntactic and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowl- edge to advance computational methods towards ever higher levels of cognitive competence. The biennial BICS Conference series (with BICS 2006 due to be held in Lesvos, Greece from 10 to 14 October 2006) aims to become a major point of contact for research scientists, engineers and practitioners throughout the world in the fields of cognitive and computational systems inspired by the brain and biology. Selected BICS 2004 authors were invited to contribute to this Special Issue on the basis of originality, technical quality and relevance of the papers presented at the BICS 2004 Conference. All invited papers have been subjected to the usual rigorous peer review process by anonymous referees. The Guest Editors are convinced that this selection of papers provides the reader with an up to date account of the latest research, development and ideas in the wide arena of disciplines encompassed under the heading of BICS 2004. The papers can be grouped into the following three categories: (1) Neural Computation (NC) covering pro- gress in neural systems, (2) Biologically Inspired Systems (BIS) covering broader issues in biological inspiration and neuromorphic systems, and (3) Cognitive Neuroscience (CNS), covering both computational models of the brain and brain inspired algorithms and artefacts. (1) Neural Computation (NC): In the first paper of this category, Fukushima and Kikuchi describe a new neural network based approach for extracting the axes of symmetry from 2-dimensional monochrome images. This uses a feedforward network. Pichevar and Rouat develop a new dynamic link architecture, aimed at invariant pattern recognition. This uses a two layer oscillatory network, and is intended for segmentation and correspondence problems. O’Keefe, Hodge and Austin introduce a neural network-based binary decision table algorithm. The underlying architecture is shown to be fast, and of wide applicability. Han describes a mixed signal VLSI implementation for large scale neural networks, using a transconductance amplifier for analogue multiplication, with neuron to neuron communication using spikes. Zayed, Hussain and Abdullah present a new multiple controller based computational paradigm for complex plant control, using a radial basis function based generalized learning model. Simulation results suggest that such adaptive non-linear techniques are more effective in tracking set point changes and for coping with sudden disturbances. The last paper in this category is by Abdullah and Hussain who develop a graph drawing bi-clustering technique (clustering records and attributes concur- rently) which shows performance superior to other bi- clustering techniques. (2) Biologically Inspired Systems (BIS): Bonzano, Bove and Martinoia describe the effects of neurochemical agents on synaptic transmission. This work was done using multi-electrode array (MEA) technology, on cultured networks of cortical neurons from rat embryos. This pioneering work starts to illustrate the convergence of artificial and real neuronal networks. Yang and Murray’s paper describes a simulation which analyses optical flow fields using adaptive integrate- and-fire neurons. This system is able to reconstruct the edge-associated three-dimensional depth from a two- dimensional image sequence and the use of adaptivity helps to reject spurious edges. Wu, McGinnity, Maguire, Glackin and Belatreche discuss the effects of weight value constraints on spike-based neural net- works trained under a form of back-propagation. Garforth, McHale and Meehan discuss action selection using a neural paradigm in a robotic context. Lesion studies suggest correspondences between the robot and humans suffering executive attention deficits. Sanchez, Navarro and Canedo model brain nuclei which process somatosensory information. Inter-neurons permit spa- tio-temporal progressive coding of information. Hir- ayama, Joshimoto and Ishii develop a new hierarchical Bayesian inference technique, balancing plasticity and stability, and yielding a straightforward learning rule. ARTICLE IN PRESS www.elsevier.com/locate/neucom 0925-2312/$ - see front matter r 2006 Published by Elsevier B.V. doi:10.1016/j.neucom.2006.05.005
-
Upload
amir-hussain -
Category
Documents
-
view
212 -
download
0
Transcript of Brain inspired cognitive systems (BICS 2004)
ARTICLE IN PRESS
0925-2312/$ - se
doi:10.1016/j.ne
Neurocomputing 69 (2006) 1825–1826
www.elsevier.com/locate/neucom
Guest Editorial
Brain inspired cognitive systems (BICS 2004)
This Special Issue is based on selected, expanded andsignificantly revised versions of papers presented at the firstInternational Conference on Brain Inspired CognitiveSystems (BICS 2004) held at Stirling, 29 August–1September 2004. The aim of BICS 2004 was to bringtogether leading scientists and engineers who use analytic,syntactic and computational methods both to understandthe prodigious processing properties of biological systemsand, specifically, of the brain, and to exploit such knowl-edge to advance computational methods towards everhigher levels of cognitive competence. The biennial BICSConference series (with BICS 2006 due to be held inLesvos, Greece from 10 to 14 October 2006) aims tobecome a major point of contact for research scientists,engineers and practitioners throughout the world in thefields of cognitive and computational systems inspired bythe brain and biology.
Selected BICS 2004 authors were invited to contribute tothis Special Issue on the basis of originality, technicalquality and relevance of the papers presented at the BICS2004 Conference. All invited papers have been subjected tothe usual rigorous peer review process by anonymousreferees. The Guest Editors are convinced that thisselection of papers provides the reader with an up to dateaccount of the latest research, development and ideas in thewide arena of disciplines encompassed under the headingof BICS 2004.
The papers can be grouped into the following threecategories: (1) Neural Computation (NC) covering pro-gress in neural systems, (2) Biologically Inspired Systems(BIS) covering broader issues in biological inspiration andneuromorphic systems, and (3) Cognitive Neuroscience(CNS), covering both computational models of the brainand brain inspired algorithms and artefacts.
(1)
Neural Computation (NC): In the first paper of thiscategory, Fukushima and Kikuchi describe a newneural network based approach for extracting the axesof symmetry from 2-dimensional monochrome images.This uses a feedforward network. Pichevar and Rouatdevelop a new dynamic link architecture, aimed atinvariant pattern recognition. This uses a two layeroscillatory network, and is intended for segmentationand correspondence problems. O’Keefe, Hodge ande front matter r 2006 Published by Elsevier B.V.
ucom.2006.05.005
Austin introduce a neural network-based binarydecision table algorithm. The underlying architectureis shown to be fast, and of wide applicability. Handescribes a mixed signal VLSI implementation for largescale neural networks, using a transconductanceamplifier for analogue multiplication, with neuron toneuron communication using spikes. Zayed, Hussainand Abdullah present a new multiple controller basedcomputational paradigm for complex plant control,using a radial basis function based generalized learningmodel. Simulation results suggest that such adaptivenon-linear techniques are more effective in tracking setpoint changes and for coping with sudden disturbances.The last paper in this category is by Abdullah andHussain who develop a graph drawing bi-clusteringtechnique (clustering records and attributes concur-rently) which shows performance superior to other bi-clustering techniques.
(2)
Biologically Inspired Systems (BIS): Bonzano, Boveand Martinoia describe the effects of neurochemicalagents on synaptic transmission. This work was doneusing multi-electrode array (MEA) technology, oncultured networks of cortical neurons from ratembryos. This pioneering work starts to illustrate theconvergence of artificial and real neuronal networks.Yang and Murray’s paper describes a simulation whichanalyses optical flow fields using adaptive integrate-and-fire neurons. This system is able to reconstruct theedge-associated three-dimensional depth from a two-dimensional image sequence and the use of adaptivityhelps to reject spurious edges. Wu, McGinnity,Maguire, Glackin and Belatreche discuss the effects ofweight value constraints on spike-based neural net-works trained under a form of back-propagation.Garforth, McHale and Meehan discuss action selectionusing a neural paradigm in a robotic context. Lesionstudies suggest correspondences between the robot andhumans suffering executive attention deficits. Sanchez,Navarro and Canedo model brain nuclei which processsomatosensory information. Inter-neurons permit spa-tio-temporal progressive coding of information. Hir-ayama, Joshimoto and Ishii develop a new hierarchicalBayesian inference technique, balancing plasticity andstability, and yielding a straightforward learning rule.
ARTICLE IN PRESSGuest Editorial / Neurocomputing 69 (2006) 1825–18261826
Cosp, Madrenas, Lucas, Alarcon, Vidal and Villar havedeveloped a neuromorphic analogue vision systemconsisting of phototransduction, non-linear filtering,oscillatory segmentation network with some post-processing. Bartolozzi and Indiveri continue to advancethe neuromorphic area, and have developed a selectiveattention chip for selective attention based on a newwinner-takes-all architecture.
(3)
Cognitive Neuroscience (CNS): Fragopanagos andTaylor describe recent work on an engineeringapproach to attention. This is biased by emotionalvalence, and is based on a structural brain model.Moura and Bonzon further develop Baars’ ‘‘GlobalWorkspace’’ theory of Consciousness, resulting inunconscious processor coalitions that result in thecreation of contexts. Bohme, Kraus, Barth andMartinetz analyse predictability of eye movementsusing video recordings of natural scenes. Locationswhich are fixated frequently are used to predictindividual eye movements. Finally, Hartley, Taylorand Taylor analyse a calcium dynamics based substratefor spike-time dependent plasticity (STDP), noting thatit provides good agreement with triplet and quadrupletdata.The Editors would like to thank all the authors for theirexcellent contributions and the anonymous reviewers for
their constructive comments and suggestions which greatlyhelped improve the quality of the published papers. Specialthanks go to Dr. Tom Heskes for inviting us to edit thisSpecial Issue, and to Vera Kamphuis from the Neuro-computing Editorial Office for her kind support in puttingthe Special Issue together.
Guest Editor
Amir HussainCentre for Cognitive and Computational Neuroscience,
University of Stirling, Stirling, Scotland, UK
E-mail address: [email protected] (A. Hussain)
Co-Guest Editors
Leslie S. SmithCentre for Cognitive and Computational Neuroscience,
University of Stirling, Stirling, Scotland, UK
E-mail address: [email protected] (L.S. Smith)
Igor AlexanderDepartment of Electrical and Electronic Engineering,
Imperial College, London, UK
E-mail address: [email protected](I. Alexander)
