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Cortical Computation via Iterative Constructions [article]

Christos Papadimitrou, Samantha Petti, Santosh Vempala
2016 arXiv   pre-print
This restricted type of construction needs little global coordination or control and thus is a candidate for neurally feasible computation.  ...  We study Boolean functions of an arbitrary number of input variables that can be realized by simple iterative constructions based on constant-size primitives.  ...  Learning So far we have studied the realizability of thresholds via neurally plausible simple iterative constructions. These constructions were based on prior knowledge of the target threshold.  ... 
arXiv:1602.08357v2 fatcat:aqyemzqhorh7po2cepg7n3zmeq

Uncertainty quantification in transcranial magnetic stimulation

Luis J. Gomez, Abdulkadir C. Yucel, Luis Hernandez-Garcia, Eric Michielssen
2013 2013 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)  
The HDMR technique generates the surrogate models using a series of iteratively constructed component functions.  ...  Next, these surrogate models are used in lieu of a computationally expensive electromagnetic simulator while obtaining the statistics of the stimulated regions via traditional Monte-Carlo method.  ...  The HDMR technique generates the surrogate models using a series of iteratively constructed component functions.  ... 
doi:10.1109/usnc-ursi.2013.6715308 fatcat:6jllz6ofobhyzkkz5n7dixozhq

Riemannian Metric Optimization for Connectivity-Driven Surface Mapping [chapter]

Jin Kyu Gahm, Yonggang Shi
2016 Lecture Notes in Computer Science  
We demonstrate our method on the mapping of thalamic surfaces according to connectivity to ten cortical regions, which we compute with the multi-shell diffusion imaging data from the Human Connectome Project  ...  The proposed RMOS approach achieves this goal by iteratively optimizing the Riemannian metric on surfaces to match the connectivity features in the LB embedding space.  ...  We then apply both RMOS and CMOS to the 212 thalamic surfaces from the HCP data and constructed average connectivity maps to the ten cortical regions.  ... 
doi:10.1007/978-3-319-46720-7_27 pmid:28083569 pmcid:PMC5223768 fatcat:xpbr3dangzg2ta7fqancgizkra

Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter

Moo K. Chung, Kim M. Dalton, Li Shen, Alan C. Evans, Richard J. Davidson
2007 IEEE Transactions on Medical Imaging  
Within the WFS framework, gray matter density and cortical thickness are also computed. Then statistical parametric maps (SPM) are constructed to localize the regions of abnormal gray matter.  ...  The mesh construction and discrete thickness computation procedures introduce substantial noise in the thickness measure [9] (Fig. 6 ).  ... 
doi:10.1109/tmi.2007.892519 pmid:17427743 fatcat:gr6ecl63gzhzdfoc4355s3li5e

Tensor-Based Cortical Surface Morphometry via Weighted Spherical Harmonic Representation

M.K. Chung, K.M. Dalton, R.J. Davidson
2008 IEEE Transactions on Medical Imaging  
The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh.  ...  Index Terms-Cortical surface, spherical harmonic (SPHARM), tensor-based morphometry.  ...  Based on a new iterative formulation, spatial derivatives of the weighted-SPHARM are computed and used to derive metric tensors and an area element.  ... 
doi:10.1109/tmi.2008.918338 pmid:18672431 fatcat:4uhueuthgvgkrf4kpoatcun2je

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Vadas Gintautas, Michael I. Ham, Benjamin Kunsberg, Shawn Barr, Steven P. Brumby, Craig Rasmussen, John S. George, Ilya Nemenman, Luís M. A. Bettencourt, Garret T. Kenyon, Olaf Sporns
2011 PLoS Computational Biology  
Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least 37.5 ms of cortical processing time.  ...  by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images.  ...  For each iteration, activity was multiplied by the local support, computed via linear convolution of the previous output activity with the ODD kernel.  ... 
doi:10.1371/journal.pcbi.1002162 pmid:21998562 pmcid:PMC3188484 fatcat:qmhauub2abahzjlkfygmwxk2ka

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

Yonggang Shi, Rongjie Lai, Danny J. J. Wang, Daniel Pelletier, David Mohr, Nancy Sicotte, Arthur W. Toga
2014 IEEE Transactions on Medical Imaging  
The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding.  ...  Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion.  ...  Note that they are fixed in subsequent iterations. After that, we iteratively optimize the weight to find the group-wise atlas. At each iteration, we compute the eigen-system of .  ... 
doi:10.1109/tmi.2014.2313812 pmid:24686245 pmcid:PMC4079755 fatcat:h26iimhxyndeznz75tw24lzlza

Cortical thickness analysis in autism with heat kernel smoothing

Moo K. Chung, Steven M. Robbins, Kim M. Dalton, Richard J. Davidson, Andrew L. Alexander, Alan C. Evans
2005 NeuroImage  
As an illustration, we apply our methods in detecting the regions of abnormal cortical thickness in 16 high functioning autistic children via random field based multiple comparison correction that utilizes  ...  We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold.  ...  Bottom: average template constructed via the improved surface warping method providing more detailed anatomy. deformable surface algorithm .  ... 
doi:10.1016/j.neuroimage.2004.12.052 pmid:15850743 fatcat:ibtpeotamjckhon75x5phyggum

Synchrony is stubborn in feedforward cortical networks

Idan Segev
2003 Nature Neuroscience  
So we should celebrate this innovative marriage between real neurons and the computer. It enables one to construct semi-realistic cortical networks of different size and architecture.  ...  This process was then iterated (Fig. 1a) .  ... 
doi:10.1038/nn0603-543 pmid:12771956 fatcat:pa2xehtdevbd7ktytbwew6jlg4

Constructing statistically unbiased cortical surface templates using feature-space covariance

Prasanna Parvathaneni, Ilwoo Lyu, Justin A. Blaber, Yuankai Huo, Allison E. Hainline, Neil D. Woodward, Hakmook Kang, Bennett A. Landman, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual  ...  The mean surface is computed by applying the weights obtained from an inverse covariance matrix, which guarantees that multiple representations from similar groups (e.g., involving imaging, demographic  ...  subdivision. c) The covariance matrix is constructed. d) The weighted mean of features is computed based on weights from covariance matrix. e) The unweighted mean is computed f) Qualitative and quantitative  ... 
doi:10.1117/12.2293641 pmid:29887664 pmcid:PMC5992907 dblp:conf/miip/ParvathaneniLHB18 fatcat:4spvrczkzrfctfjostiai7ugmm

A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation [chapter]

Sarah Parisot, Martin Rajchl, Jonathan Passerat-Palmbach, Daniel Rueckert
2015 Lecture Notes in Computer Science  
Parcellation of the cortical surface into distinct regions is an essential step in order to construct such networks.  ...  In this paper, we propose a flexible continuous flow maximisation approach for connectivity driven parcellation that iteratively updates the parcels' boundaries and centres based on connectivity information  ...  Therefore, network construction requires an initial parcellation stage of the cortical surface into distinct regions.  ... 
doi:10.1007/978-3-319-24574-4_20 fatcat:oyqdq4nilfeh3lq4f7etnch2pa

Modeling the development of maps of complex cells in V1

Jan Antolik, JA Bednar
2009 BMC Neuroscience  
Initially all weights in the model are random, and each is modified via a Hebbian learning rule.  ...  In this work, we model V1 as two topographically organized sheets, one representing cortical layer 4 and one representing layer 2/3. Only layer 4 receives direct thalamic input.  ...  Initially all weights in the model are random, and each is modified via a Hebbian learning rule.  ... 
doi:10.1186/1471-2202-10-s1-p60 fatcat:6yvl6fon6jbrdelczjbnwiru7y

Accurate prediction of V1 location from cortical folds in a surface coordinate system

Oliver P. Hinds, Niranjini Rajendran, Jonathan R. Polimeni, Jean C. Augustinack, Graham Wiggins, Lawrence L. Wald, H. Diana Rosas, Andreas Potthast, Eric L. Schwartz, Bruce Fischl
2008 NeuroImage  
National Institute for Neurological Disorders and Stroke (R01 NS052585), as well as the Mental Illness and Neuroscience Discovery (MIND) Institute and is part of the National Alliance for Medical Image Computing  ...  An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space.  ...  Results Iterations of template generation To determine the number of productive iterations of template generation, V1 alignment quality was computed for between one and four iterations.  ... 
doi:10.1016/j.neuroimage.2007.10.033 pmid:18055222 pmcid:PMC2258215 fatcat:aljlwjfsxngjnek4ybateybczi

Persistent Reeb Graph Matching for Fast Brain Search [chapter]

Yonggang Shi, Junning Li, Arthur W. Toga
2014 Lecture Notes in Computer Science  
The key idea is to compactly represent and quantify the differences of cortical surfaces in terms of their intrinsic geometry by comparing the Reeb graphs constructed from their Laplace-Beltrami eigenfunctions  ...  Given the Reeb graphs of two cortical surfaces, our method can calculate their distance in less than 10 milliseconds on a PC.  ...  In section 2, we introduce the LB eigenfunctions of cortical surfaces and the construction of their Reeb graphs.  ... 
doi:10.1007/978-3-319-10581-9_38 pmid:25594076 pmcid:PMC4292885 fatcat:hjqrpuyqn5ailbpmjec4jpezlu

Direct cortical mapping via solving partial differential equations on implicit surfaces

Y SHI, P THOMPSON, I DINOV, S OSHER, A TOGA
2007 Medical Image Analysis  
Starting from a properly designed initial map, we compute the map iteratively by solving a partial differential equation (PDE) defined on the source cortical surface.  ...  In this paper, we propose a novel approach for cortical mapping that computes a direct map between two cortical surfaces while satisfying constraints on sulcal landmark curves.  ...  Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.  ... 
doi:10.1016/j.media.2007.02.001 pmid:17379568 pmcid:PMC2227953 fatcat:fo6yx25t75fghb6wzyq4pxc2au
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