Connectionists: CFP: Neural Networks Special Issue on Multi-scale, Multi-modal Neural Mode, ling and Simulation

[Shin Ishii]

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CALL FOR PAPERS
2011 Special Issue of Neural Networks
Multi-Scale, Multi-Modal Neural Modeling and Simulation
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Genuine understanding of brain function requires integration of
knowledge at multiple levels, from the whole brain network to local
circuits, single neurons, genes and molecules. Recent advances in
high-throughput measurement and selective manipulation, such as optical
imaging, computerized anatomy, proteome and transcriptome, cell-type
specific gene manipulation and optogenetic stimulation provide rich data
prompting us to build quantitative models of many kinds. Yet it still
remains a big challenge to link findings form different levels, such as
predicting how a certain genetic variation increases the risk of some
cognitive disorders. Coherent understanding of the brain function
requires integration of heterogeneous models, such as large-scale
network models, compartmental single neuron models, intracellular
signaling cascades and gene networks, that work in difference spatial
and temporal scales with different governing equations.

The goal of this special issue is to bring together the latest advances
in integration of neural models at different levels and to promote
application of methods and concepts derived in one level to other
levels. Specific topics include, but not limited to: 1) how to interface
models describing different physical processes, such as network
dynamics, cellular electric activities, molecular reactions, and
morphological changes; 2) how to efficiently compute combined models
working in different temporal and spatial scales; 3) how to build a
simplified model that abstracts the essential features of a finer-scale
model; 4) how to estimate unknown parameters, validate the predictive
power of a complex model, and analyze its behaviors systematically.
Papers addressing not only technical advances but also novel insights
gained from multi-scale, multi-modal model integration are encouraged.

Guest Editors:
    Shin Ishii (Kyoto University)
    Marcus Diesmann (RIKEN Brain Science Institute)
    Kenji Doya (Okinawa Institute of Science and Technology)
Submission instructions can be found at:
    http://ees.elsevier.com/neunet/
Important Dates
    Submission deadline: December 1st, 2010
    Revised submission deadline: March 1st, 2011
    Final decision: June 1st, 2011
    Publication: fall 2011
Contact:
    Neural Networks Okinawa Office
    e-mail: nneo at oist.jp