[Comp-neuro] NEURAL COMPUTATION - January 1, 2015

Terry Sejnowski terry at salk.edu
Tue Dec 30 19:58:10 CET 2014


Neural Computation was founded with the goal of providing a home for the 
best research in computational approaches to understanding brain function.  

With this issue Neural Computation is now all electronic 
(color illustrations are free) and also has a broader scope.  

The goal of the BRAIN Initiative, announced by President Obama on April 2, 2013, 
is to accelerate progress in understanding basic principles of brain function 
by developing innovative neurotechnologies.  The BRAIN 2025 report on the BRAIN 
Initiative highlighted Theory, Modeling, Computation and Statistics (TMCS) 
as essential to this goal (http://www.braininitiative.nih.gov/2025/index.htm).  

The neurotechniques developed by the BRAIN Initiative will scale up the acquisition 
of data by three orders of magnitude in the next decade.  Every area of neuroscience, 
from molecular to systems, can benefit from advanced computational techniques to analyze, 
model, and interpret these data, serving as the foundation for conceptual advances 
in brain theories.    

Neural Computation is uniquely positioned at the crossroads between Neuroscience 
and TMCS and welcomes the submission of original papers from all areas of TMCS, 

* Advanced experimental design
* Analysis of chemical sensor data
* Connectomic reconstructions
* Analysis of multielectrode and optical recordings
* Genetic data for cell identity 
* Analysis of behavioral data 
* Multiscale models
* Analysis of molecular mechanisms
* Neuroinformatics
* Analysis of brain imaging data
* Neuromorphic engineering 
* Principles of neural coding, computation, circuit dynamics, and plasticity
* Theories of brain function

An expanded editorial board will guide Neural Computation in this broader arena: 

As the US BRAIN Initiative and the European Human Brain Project continue to expand, 
and as other countries launch new brain programs, Neural Computation will be 
central in integrating these international efforts.

Terry Sejnowski


Neural Computation - Volume 27, Number 1 - January 1, 2015

Available online for download now:



Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron 
and Ensemble Data Analysis 
Carlos E. Vargas-Irwin, David M. Brandman, Jonas B. Zimmermann, 
John P. Donoghue, Michael J. Black


Optimizing the Representation of Orientation Preference Maps in Visual Cortex 
Nicholas J. Hughes, Geoffrey J. Goodhill


Topological Sparse Learning of Dynamic Form Patterns 
T. Guthier, V. Willert, J. Eggert

Dynamics of Gamma Bursts in Local Field Potentials 
Priscilla E. Greenwood, Mark D. McDonnell, Lawrence M. Ward

Spatiotemporal Conditional Inference and Hypothesis Tests 
for Neural Ensemble Spiking Precision 
Matthew T. Harrison, Asohan Amarasingham, Wilson Truccolo

Toward a Multisubject Analysis of Neural Connectivity 
C. J. Oates, L. Costa, T. E. Nichols

Using Multilayer Perceptron Computation to Discover Ideal 
Insect Olfactory Receptor Combinations in the Mosquito and 
Fruit Fly for an Efficient Electronic Nose 
Luqman R. Bachtiar, Charles P. Unsworth, Richard D. Newcomb

Graph Degree Sequence Solely Determines the Expected Hopfield Network 
Pattern Stability 
Daniel Berend, Shlomi Dolev, Ariel Hanemann

Efficient Training of Convolutional Deep Belief Networks in the 
Frequency Domain for Application to High-Resolution 2D and 3D Images 
Tom Brosch, Roger Tam

Conditional Density Estimation with Dimensionality Reduction via 
Squared-Loss Conditional Entropy Minimization 
Voot Tangkaratt, Ning Xie, Masashi Sugiyama


ON-LINE -- http://www.mitpressjournals.org/neuralcomp


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