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biblio_sparse.bib
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Laurent Perrinet at 2017-01-20 22:51:24 -0300
%% Saved with string encoding Unicode (UTF-8)
@article{PerrinetBednar15,
Author = {Perrinet, Laurent U. and Bednar, James A.},
Date-Added = {2017-01-21 00:48:31 +0000},
Date-Modified = {2017-01-21 00:48:31 +0000},
Doi = {10.1038/srep11400},
Journal = {Scientific Reports},
Keywords = {assofield},
Note = {In print},
Pages = {11400},
Posted-At = {2015-03-26 08:59:35},
Title = {Edge co-occurrences can account for rapid categorization of natural versus animal images},
Url = {http://www.nature.com/articles/srep11400},
Volume = 5,
Year = {2015},
Bdsk-Url-1 = {http://www.nature.com/articles/srep11400},
Bdsk-Url-2 = {http://dx.doi.org/10.1038/srep11400}}
@inbook{Perrinet15sparse,
Author = {Perrinet, Laurent U.},
Booktitle = {Biologically inspired computer vision},
Chapter = {14},
Citeulike-Article-Id = {13514904},
Date-Added = {2017-01-21 00:44:27 +0000},
Date-Modified = {2017-01-21 00:44:27 +0000},
Doi = {10.1002/9783527680863.ch14},
Editor = {Crist{\'{o}}bal, Gabriel and Keil, Matthias S. and Perrinet, Laurent U.},
Isbn = {9783527680863},
Keywords = {bicv-sparse, sanz12jnp, vacher14},
Month = nov,
Priority = {0},
Publisher = {Wiley-VCH Verlag GmbH {\&} Co. KGaA},
Title = {Sparse Models for Computer Vision},
Url = {http://onlinelibrary.wiley.com/doi/10.1002/9783527680863.ch14/summary},
Year = {2015},
Bdsk-Url-1 = {http://onlinelibrary.wiley.com/doi/10.1002/9783527680863.ch14/summary},
Bdsk-Url-2 = {http://dx.doi.org/10.1002/9783527680863.ch14}}
@article{Perrinet12pred,
Abstract = {In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physio-logy and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independent of their texture. Second, we observe that incoherent features are explained away, while coherent information diffuses progressively to the global scale. Most previous models included ad hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights into the role of prediction underlying a large class of sensory computations.},
Archiveprefix = {arXiv},
Author = {Perrinet, Laurent U. and Masson, Guillaume S.},
Citeulike-Article-Id = {10834812},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1162/NECO\_a\_00332},
Citeulike-Linkout-1 = {http://arxiv.org/abs/1208.6471},
Citeulike-Linkout-2 = {http://arxiv.org/pdf/1208.6471},
Citeulike-Linkout-3 = {http://dx.doi.org/10.1162/neco\_a\_00332},
Citeulike-Linkout-4 = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO\_a\_00332},
Citeulike-Linkout-5 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472550/},
Citeulike-Linkout-6 = {http://view.ncbi.nlm.nih.gov/pubmed/22734489},
Citeulike-Linkout-7 = {http://www.hubmed.org/display.cgi?uids=22734489},
Date-Added = {2013-12-25 18:24:41 +0000},
Date-Modified = {2013-12-25 18:24:52 +0000},
Day = {31},
Doi = {10.1162/NECO\_a\_00332},
Eprint = {1208.6471},
Issn = {1530-888X},
Journal = {Neural Computation},
Keywords = {aperture-problem, khoei13jpp, perrinet12pred, predictive-coding; thesis},
Month = oct,
Number = {10},
Pages = {2726--2750},
Pmcid = {PMC3472550},
Pmid = {22734489},
Priority = {0},
Publisher = {MIT Press},
Title = {{Motion-Based} Prediction Is Sufficient to Solve the Aperture Problem},
Url = {http://dx.doi.org/10.1162/NECO\_a\_00332},
Volume = {24},
Year = {2012},
Bdsk-Url-1 = {http://dx.doi.org/10.1162/NECO%5C_a%5C_00332}}
@article{Babadi14,
Abstract = {In several sensory pathways, input stimuli project to sparsely active downstream populations that have more neurons than incoming axons. Here, we address the computational benefits of expansion and sparseness for clustered inputs, where different clusters represent behaviorally distinct stimuli and intracluster variability represents sensory or neuronal noise. Through analytical calculations and numerical simulations, we show that expansion implemented by feed-forward random synaptic weights amplifies variability in the incoming stimuli, and this noise enhancement increases with sparseness of the expanded representation. In addition, the low dimensionality of the input layer generates overlaps between the induced representations of different stimuli, limiting the benefit of expansion. Highly sparse expansive representations obtained through synapses that encode the clustered structure of the input reduce both intrastimulus variability and the excess overlaps between stimuli, enhancing the ability of downstream neurons to perform classification and recognition tasks. Implications for olfactory, cerebellar, and visual processing are discussed.},
Author = {Babadi, Baktash and Sompolinsky, Haim},
Citeulike-Article-Id = {13434451},
Citeulike-Linkout-0 = {http://www.cell.com/neuron/abstract/S0896-6273(14)00646-1},
Citeulike-Linkout-1 = {http://dx.doi.org/10.1016/j.neuron.2014.07.035},
Date-Added = {2014-11-19 22:09:32},
Day = {19},
Doi = {10.1016/j.neuron.2014.07.035},
Issn = {08966273},
Journal = {Neuron},
Keywords = {bicv-sparse, sensory-coding, sparse\_coding},
Month = nov,
Number = {5},
Pages = {1213--1226},
Priority = {2},
Publisher = {Elsevier},
Title = {Sparseness and Expansion in Sensory Representations},
Url = {http://dx.doi.org/10.1016/j.neuron.2014.07.035},
Volume = {83},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2014.07.035}}
@article{vonderHeydt84,
Abstract = {
Figures in which human observers perceive "illusory contours" were found to evoke responses in cells of area 18 in the visual cortex of alert monkeys. The cells responded as if the contours were formed by real lines or edges. Modifications that weakened the perception of contours also reduced the neuronal responses. In contrast, cells in area 17 were apparently unable to "see" these contours.
},
Author = {von der Heydt, R. and Peterhans, E. and Baumgartner, G.},
Citeulike-Article-Id = {441291},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1126/science.6539501},
Citeulike-Linkout-1 = {http://www.sciencemag.org/content/224/4654/1260.abstract},
Citeulike-Linkout-2 = {http://www.sciencemag.org/content/224/4654/1260.full.pdf},
Citeulike-Linkout-3 = {http://www.sciencemag.org/cgi/content/abstract/224/4654/1260},
Citeulike-Linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/6539501},
Citeulike-Linkout-5 = {http://www.hubmed.org/display.cgi?uids=6539501},
Date-Added = {2014-11-04 13:37:41},
Day = {15},
Doi = {10.1126/science.6539501},
Issn = {0036-8075},
Journal = {Science (New York, N.Y.)},
Keywords = {area-v1, area-v2, bicv-sparse},
Month = jun,
Number = {4654},
Pages = {1260--1262},
Pmid = {6539501},
Priority = {2},
Publisher = {American Association for the Advancement of Science},
Title = {Illusory contours and cortical neuron responses.},
Url = {http://dx.doi.org/10.1126/science.6539501},
Volume = {224},
Year = {1984},
Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.6539501}}
@inbook{Grossberg84,
Author = {Grossberg, Stephen},
Citeulike-Article-Id = {13418055},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/s0166-4115(08)62080-4},
Date-Added = {2014-11-04 13:34:55},
Doi = {10.1016/s0166-4115(08)62080-4},
Isbn = {9780444875129},
Keywords = {bicv-sparse},
Pages = {59--86},
Priority = {2},
Publisher = {Elsevier},
Title = {Outline of A Theory of Brightness, Color, and form Perception},
Url = {http://dx.doi.org/10.1016/s0166-4115(08)62080-4},
Volume = {20},
Year = {1984},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/s0166-4115(08)62080-4}}
@article{Laughlin81,
Abstract = {The contrast-response function of a class of first order interneurons in the fly's compound eye approximates to the cumulative probability distribution of contrast levels in natural scenes. Elementary information theory shows that this matching enables the neurons to encode contrast fluctuations most efficiently.},
Author = {Laughlin, S.},
Citeulike-Article-Id = {688543},
Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/7303823},
Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=7303823},
Comment = {see Srinivasan82 and Hosoya05},
Date-Added = {2014-11-04 13:34:08},
Issn = {0341-0382},
Journal = {Zeitschrift f\"{u}r Naturforschung. Section C: Biosciences},
Keywords = {bicv-sparse},
Number = {9-10},
Pages = {910--912},
Pmid = {7303823},
Priority = {0},
Title = {A simple coding procedure enhances a neuron's information capacity.},
Url = {http://view.ncbi.nlm.nih.gov/pubmed/7303823},
Volume = {36},
Year = {1981},
Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/7303823}}
@article{Keil00,
Author = {Keil, Matthias S. and Crist\'{o}bal, Gabriel},
Citeulike-Article-Id = {13418052},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1364/josaa.17.000697},
Date-Added = {2014-11-04 13:24:36},
Doi = {10.1364/josaa.17.000697},
Issn = {1084-7529},
Journal = {Journal of the Optical Society of America A},
Keywords = {bicv-sparse},
Number = {4},
Pages = {697+},
Priority = {2},
Title = {Separating the chaff from the wheat: possible origins of the oblique effect},
Url = {http://dx.doi.org/10.1364/josaa.17.000697},
Volume = {17},
Year = {2000},
Bdsk-Url-1 = {http://dx.doi.org/10.1364/josaa.17.000697}}
@article{SenGupta14,
Author = {SenGupta, B. and Stemmler, M. B.},
Citeulike-Article-Id = {13417823},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1109/jproc.2014.2307755},
Citeulike-Linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=6797884},
Date-Added = {2014-11-04 09:01:16},
Doi = {10.1109/jproc.2014.2307755},
Institution = {Wellcome Trust Centre for Neuroimaging, Univ. Coll. London, London, UK},
Issn = {0018-9219},
Journal = {Proceedings of the IEEE},
Keywords = {bicv-sparse, efficiency, sparse\_coding},
Month = may,
Number = {5},
Pages = {738--750},
Priority = {2},
Publisher = {IEEE},
Title = {Power Consumption During Neuronal Computation},
Url = {http://dx.doi.org/10.1109/jproc.2014.2307755},
Volume = {102},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1109/jproc.2014.2307755}}
@article{Babadi2014Sparseness,
Abstract = {In several sensory pathways, input stimuli project to sparsely active downstream populations that have more neurons than incoming axons. Here, we address the computational benefits of expansion and sparseness for clustered inputs, where different clusters represent behaviorally distinct stimuli and intracluster variability represents sensory or neuronal noise. Through analytical calculations and numerical simulations, we show that expansion implemented by feed-forward random synaptic weights amplifies variability in the incoming stimuli, and this noise enhancement increases with sparseness of the expanded representation. In addition, the low dimensionality of the input layer generates overlaps between the induced representations of different stimuli, limiting the benefit of expansion. Highly sparse expansive representations obtained through synapses that encode the clustered structure of the input reduce both intrastimulus variability and the excess overlaps between stimuli, enhancing the ability of downstream neurons to perform classification and recognition tasks. Implications for olfactory, cerebellar, and visual processing are discussed. Copyright {\copyright} 2014 Elsevier Inc. All rights reserved.},
Author = {Babadi, Baktash and Sompolinsky, Haim},
Citeulike-Article-Id = {13353648},
Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/25155954},
Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=25155954},
Date-Added = {2014-09-09 14:12:47},
Day = {3},
Issn = {1097-4199},
Journal = {Neuron},
Keywords = {bicv-sparse, sensory},
Month = sep,
Number = {5},
Pages = {1213--1226},
Pmid = {25155954},
Priority = {4},
Title = {Sparseness and expansion in sensory representations.},
Url = {http://view.ncbi.nlm.nih.gov/pubmed/25155954},
Volume = {83},
Year = {2014},
Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/25155954}}
@article{PerrinetAdamsFriston14,
Abstract = {This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements---in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system---like the oculomotor system---tries to control its environment with delayed signals.},
Author = {Perrinet, Laurent U. and Adams, Rick A. and Friston, Karl J.},
Booktitle = {Biological Cybernetics},
Citeulike-Article-Id = {13329753},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/s00422-014-0620-8},
Citeulike-Linkout-1 = {http://link.springer.com/article/10.1007/s00422-014-0620-8},
Date-Added = {2014-08-19 08:25:45},
Day = {16},
Doi = {10.1007/s00422-014-0620-8},
Issn = {1432-0770},
Journal = {Biological Cybernetics},
Keywords = {active-inference, bayesian, bicv-motion, bicv-sparse, delays, eye, eye-movements, free-energy, generalized-coordinates, generalized-filtering, oculomotor, perception, perrinetadamsfriston14, smooth-pursuit, tracking-eye-movements, variational-filtering},
Month = dec,
Number = {6},
Pages = {777--801},
Priority = {0},
Publisher = {Springer Berlin Heidelberg},
Title = {Active inference, eye movements and oculomotor delays},
Url = {http://dx.doi.org/10.1007/s00422-014-0620-8},
Volume = {108},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/s00422-014-0620-8}}
@article{Baddeley96,
Abstract = {Not Available},
Author = {Baddeley, Roland},
Citeulike-Article-Id = {4891559},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/381560a0},
Citeulike-Linkout-1 = {http://adsabs.harvard.edu/cgi-bin/nph-bib\_query?bibcode=1996Natur.381..560B},
Citeulike-Linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/8637587},
Citeulike-Linkout-3 = {http://www.hubmed.org/display.cgi?uids=8637587},
Date-Added = {2014-07-28 12:04:06},
Day = {13},
Doi = {10.1038/381560a0},
Issn = {0028-0836},
Journal = {Nature},
Keywords = {area-v1, bicv-sparse, sparse},
Month = jun,
Number = {6583},
Pages = {560--561},
Pmid = {8637587},
Priority = {4},
Title = {An efficient code in {V1}?},
Url = {http://dx.doi.org/10.1038/381560a0},
Volume = {381},
Year = {1996},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/381560a0}}
@article{Baddeley97,
Author = {Baddeley, R. and Abbott, L. F. and Booth, M. C. A. and Sengpiel, F. and Freeman, T. and Wakeman, E. A. and Rolls, E. T.},
Citeulike-Article-Id = {13267577},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1098/rspb.1997.0246},
Citeulike-Linkout-1 = {http://citeseer.nj.nec.com/19262.html},
Date-Added = {2014-07-16 13:38:36},
Day = {22},
Doi = {10.1098/rspb.1997.0246},
Issn = {1471-2954},
Journal = {Proceedings of the Royal Society B: Biological Sciences},
Keywords = {bicv-sparse},
Month = dec,
Number = {1389},
Pages = {1775--1783},
Priority = {2},
Title = {Responses of neurons in primary and inferior temporal visual cortices to natural scenes},
Url = {http://citeseer.nj.nec.com/19262.html},
Volume = {264},
Year = {1997},
Bdsk-Url-1 = {http://citeseer.nj.nec.com/19262.html},
Bdsk-Url-2 = {http://dx.doi.org/10.1098/rspb.1997.0246}}
@article{Bolz89,
Abstract = {The cells in the primary visual cortex possess numerous functional properties that are more complex and varied than those seen in the cortical input. These properties result from the network of intrinsic cortical connections running across the cortical layers and between cortical columns. In the current study we relate the long receptive fields that are characteristic of layer 6 cells to the input that these cells receive from layer 5. The axons of layer 5 pyramidal cells project over long distances within layer 6, enabling layer 6 cells to collect input from regions of cortex representing large parts of the visual field. When layer 5 was locally inactivated by injection of the inhibitory transmitter {GABA}, layer 6 cells lost sensitivity over the portion of their receptive fields corresponding to the inactivated region of layer 5. This suggests that the extensive convergence in the projection from layer 5 to layer 6 is responsible for generating the long receptive fields characteristic of the layer 6 cells.},
Author = {Bolz, J{\"{u}}rgen and Gilbert, Charles D.},
Citeulike-Article-Id = {13253186},
Date-Added = {2014-07-08 11:51:13},
Journal = {European Journal of Neuroscience},
Keywords = {bicv-sparse},
Number = {3},
Pages = {263--8},
Priority = {2},
Title = {The Role of Horizontal Connections in Generating Long Receptive Fields in the Cat Visual Cortex},
Volume = {1},
Year = {1989}}
@book{Hebb49,
Address = {New York},
Author = {Hebb, Donald O.},
Citeulike-Article-Id = {13253185},
Comment = {* origin : "Actions, sensations, and states of feeling, occurring together, or in close succession, tend to grow together, or cohere, in such a way that when any of them is afterwards presented to the mind, the others are apt to be brought up in idea." (Bain, 1872, p85.) *: he \url{http://neuron-ai.tuke.sk/NCS/VOL1/P3\_html/node14.html} is a student of Lashley (engramm) * \url{http://www.smithsrisca.demon.co.uk/hebbian-theory.html} * p.62 "When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased."},
Date-Added = {2014-07-08 11:51:13},
Keywords = {bicv-sparse},
Priority = {2},
Publisher = {Wiley},
Title = {The organization of behavior: {A} neuropsychological theory},
Year = {1949}}
@article{Lee99,
Author = {Lee, Daniel D. and Seung, Sebastian H.},
Citeulike-Article-Id = {13253177},
Date-Added = {2014-07-08 11:30:20},
Journal = {Nature},
Keywords = {bicv-sparse},
Pages = {788--91},
Priority = {2},
Title = {Learning the parts of objects by non-negative matrix factorization},
Volume = {401},
Year = {1999}}
@inproceedings{Ranzato07,
Annote = {Not accepted},
Author = {Ranzato, Marc' A. and Poultney, Christopher S. and Chopra, Sumi and LeCun, Yan},
Booktitle = {Advances in neural information processing systems},
Citeulike-Article-Id = {13253176},
Date-Added = {2014-07-08 11:30:20},
Keywords = {bicv-sparse},
Pages = {1137--44},
Priority = {2},
Title = {Efficient Learning of Sparse Overcomplete Representations with an {Energy-Based} Model},
Volume = {19},
Year = {2007}}
@article{Rissanen78,
Author = {Rissanen, Jorma},
Citeulike-Article-Id = {13253163},
Date-Added = {2014-07-08 11:03:34},
Journal = {Automatica},
Keywords = {bicv-sparse},
Pages = {465--71},
Priority = {2},
Title = {Modeling By Shortest Data Description},
Volume = {14},
Year = {1978}}
@incollection{Olshausen02,
Author = {Olshausen, Bruno A.},
Booktitle = {Probabilistic {M}odels of the {B}rain: {P}erception and {N}eural {F}unction},
Chapter = {Sparse Codes and Spikes},
Citeulike-Article-Id = {13253162},
Date-Added = {2014-07-08 11:03:34},
Editor = {Rao, Rajesh P. N. and Olshausen, Bruno A. and Lewicki, Michael S.},
Keywords = {bayesian, bicv-sparse, models},
Pages = {257--72},
Priority = {2},
Publisher = {MIT Press},
Title = {Sparse Codes and Spikes},
Year = {2002}}
@article{Nikitin09,
Abstract = {The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied. We postulate, using the mammalian auditory system as an example, that the presence of a subpopulation structure within a neural population is consistent with an optimal neural code.},
Author = {Nikitin, Alexander P. and Stocks, Nigel G. and Morse, Robert P. and McDonnell, Mark D.},
Citeulike-Article-Id = {13253161},
Comment = {* emergence of discrete tuning curves * binary code for N < 3Hz},
Date-Added = {2014-07-08 11:03:34},
Eprint = {0809.1549v2},
Journal = {Physical {R}eview {L}etters},
Keywords = {bicv-sparse},
Number = {13},
Pages = {138101},
Priority = {2},
Title = {Neural Population Coding is Optimized by Discrete Tuning Curves},
Volume = {103},
Year = {2009}}
@article{Bethge03,
Author = {Bethge, Matthias and Rotermund, David and Pawelzik, Klaus},
Citeulike-Article-Id = {13253159},
Date-Added = {2014-07-08 11:03:34},
Institution = {Institute of Theoretical Physics, University of Bremen, Otto-Hahn-Allee, D-28334 Bremen, Germany},
Journal = {Physical {R}eview {L}etters},
Keywords = {bicv-sparse},
Number = {8},
Pages = {088104},
Priority = {2},
Title = {Second Order Phase Transition in Neural Rate Coding: Binary Encoding is Optimal for Rapid Signal Transmission},
Volume = {90},
Year = {2003}}
@article{Akaike74,
Author = {Akaike, Hirotugu},
Citeulike-Article-Id = {13253158},
Comment = {cf. rissanen * AIC "Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In Proc. 2nd Int. Symp. Information Theory (eds B. N. Petrov and F. Cs Jaki), pp. 267--281. Budapest "},
Date-Added = {2014-07-08 11:03:33},
Journal = {I{EEE} Transactions on Automatic Control},
Keywords = {bicv-sparse},
Pages = {716--23},
Priority = {2},
Title = {A New Look at the Statistical Model Identification},
Volume = {19},
Year = {1974}}
@book{Tikhonov77,
Address = {Washington},
Author = {Tikhonov, Andrei N.},
Citeulike-Article-Id = {4815186},
Citeulike-Linkout-0 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&\#38;path=ASIN/0470991240},
Citeulike-Linkout-1 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0470991240},
Citeulike-Linkout-10 = {http://www.librarything.com/isbn/0470991240},
Citeulike-Linkout-2 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&path=ASIN/0470991240},
Citeulike-Linkout-3 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&path=ASIN/0470991240},
Citeulike-Linkout-4 = {http://www.amazon.jp/exec/obidos/ASIN/0470991240},
Citeulike-Linkout-5 = {http://www.amazon.co.uk/exec/obidos/ASIN/0470991240/citeulike00-21},
Citeulike-Linkout-6 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0470991240},
Citeulike-Linkout-7 = {http://www.worldcat.org/isbn/0470991240},
Citeulike-Linkout-8 = {http://books.google.com/books?vid=ISBN0470991240},
Citeulike-Linkout-9 = {http://www.amazon.com/gp/search?keywords=0470991240\&index=books\&linkCode=qs},
Date-Added = {2014-07-08 10:59:52},
Isbn = {0470991240},
Keywords = {bicv-sparse, tikhonov},
Priority = {2},
Publisher = {Winston \& Sons},
Title = {Solutions of {Ill-Posed} Problems},
Url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0470991240},
Year = {1977},
Bdsk-Url-1 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20%5C&path=ASIN/0470991240}}
@article{Lewicki00,
Author = {Lewicki, Michael S. and Sejnowski, Terrence J.},
Citeulike-Article-Id = {13253128},
Date-Added = {2014-07-08 10:33:52},
Journal = {Neural {C}omputation},
Keywords = {bicv-sparse},
Number = {2},
Pages = {337--65},
Priority = {0},
Title = {Learning overcomplete representations},
Volume = {12},
Year = {2000}}
@article{Oja82,
Author = {Oja, Erkki},
Citeulike-Article-Id = {13253127},
Date-Added = {2014-07-08 10:33:52},
Journal = {Journal of {M}athematical biology},
Keywords = {bicv-sparse},
Pages = {267--73},
Priority = {0},
Title = {A {S}implified {N}euron {M}odel as a {P}rincipal {C}omponent {A}nalyzer},
Volume = {15},
Year = {1982}}
@article{Pece02,
Abstract = {Linear expansions of images find many applications in image processing and computer vision. Over- complete expansions are often desirable, as they are better models of the image-generation process. Such expansions lead to the use of sparse codes. However, minimizing the number of non-zero coefficients of linear expansions is an unsolved problem. In this article, a generative-model framework is used to analyze the requirements, the difficulty, and current approaches to sparse image coding.},
Author = {Pece, Arthur E. C.},
Citeulike-Article-Id = {13253126},
Date-Added = {2014-07-08 10:33:52},
Journal = {Journal of Mathematical Imaging and Vision},
Keywords = {bicv-sparse},
Pages = {89--108},
Priority = {0},
Title = {The problem of sparse image coding},
Volume = {17},
Year = {2002}}
@book{Mallat98,
Author = {Mallat, St{\'{e}}phane},
Booktitle = {A wavelet tour of signal processing},
Citeulike-Article-Id = {13253125},
Date-Added = {2014-07-08 10:33:52},
Edition = {Second},
Keywords = {bicv-sparse},
Priority = {0},
Publisher = {Academic Press},
Title = {A wavelet tour of signal processing},
Year = {1998}}
@article{Doi07,
Abstract = {We address the problem of robust coding in which the signal information should be preserved in spite of intrinsic noise in the representation. We present a theoretical analysis for 1- and {2-D} cases and characterize the optimal linear encoder and decoder in the mean-squared error sense. Our analysis allows for an arbitrary number of coding units, thus including both under- and over-complete representations, and provides insights into optimal coding strategies. In particular, we show how the form of the code adapts to the number of coding units and to different data and noise conditions in order to achieve robustness. We also present numerical solutions of robust coding for high-dimensional image data, demonstrating that these codes are substantially more robust than other linear image coding methods such as {PCA}, {ICA}, and wavelets.},
Author = {Doi, Eizaburo and Balcan, Doru C. and Lewicki, Michael S.},
Citeulike-Article-Id = {13253124},
Date-Added = {2014-07-08 10:33:52},
Journal = {I{EEE} {T}ransactions in {I}mage {P}rocessing},
Keywords = {bicv-sparse},
Number = {2},
Pages = {442--52},
Priority = {0},
Title = {Robust Coding over Noisy Overcomplete Channels.},
Volume = {16},
Year = {2007}}
@article{Rehn07,
Abstract = {Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields. Yet, existing models have failed to predict the diverse shapes of receptive fields that occur in nature. The existing models used a particular "soft" form of sparseness that limits average neuronal activity. Here we study models of efficient coding in a broader context by comparing soft and "hard" forms of neuronal sparseness. As a result of our analyses, we propose a novel network model for visual cortex. The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited. This form of hard sparseness also economises cortical resources like synaptic memory and metabolic energy. Furthermore, our model accurately predicts the distribution of receptive field shapes found in the primary visual cortex of cat and monkey.},
Author = {Rehn, Martin and Sommer, Friedrich T.},
Citeulike-Article-Id = {13253123},
Date-Added = {2014-07-08 10:33:52},
Journal = {Journal of {C}omputational {N}euroscience},
Keywords = {bicv-sparse, biological, coding, field, learning, receptive, sparse, vision},
Number = {2},
Pages = {135--46},
Priority = {0},
Title = {A model that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields},
Volume = {22},
Year = {2007}}
@article{Zibulevsky01,
Author = {Zibulevsky, Michael and Pearlmutter, Barak A.},
Citeulike-Article-Id = {13253122},
Date-Added = {2014-07-08 10:33:52},
Journal = {Neural {C}omputation},
Keywords = {bicv-sparse},
Number = {4},
Pages = {863--82},
Priority = {0},
Title = {Blind {S}ource {S}eparation by sparse decomposition},
Volume = {13},
Year = {2001}}
@article{Fyfe95,
Author = {Fyfe, Colin and Baddeley, Roland},
Citeulike-Article-Id = {13253121},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1088/0954-898X/6/3/002},
Date-Added = {2014-07-08 10:33:52},
Day = {1},
Doi = {10.1088/0954-898X/6/3/002},
Issn = {1361-6536},
Journal = {Network: Computation in Neural Systems},
Keywords = {bicv-sparse},
Month = aug,
Number = {3},
Pages = {333--344},
Priority = {0},
Title = {Finding compact and sparse-distributed representations of visual images},
Url = {http://dx.doi.org/10.1088/0954-898X/6/3/002},
Volume = {6},
Year = {1995},
Bdsk-Url-1 = {http://dx.doi.org/10.1088/0954-898X/6/3/002}}
@article{Hubel68,
Abstract = {1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers ({II} and the upper two-thirds of {III}) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer {III}, and in {IV} A and {IV} B. In layer {IV} B they form a large proportion of the population, whereas complex cells are rare. In layers {IV} A and {IV} B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer {IV} most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and {VI} contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer {IV}, the higher orders in the upper and lower layers.},
Author = {Hubel, D. H. and Wiesel, T. N.},
Citeulike-Article-Id = {9911891},
Citeulike-Linkout-0 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557912/},
Citeulike-Linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557912/},
Citeulike-Linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/4966457},
Citeulike-Linkout-3 = {http://www.hubmed.org/display.cgi?uids=4966457},
Date-Added = {2014-06-27 16:04:33},
Issn = {0022-3751},
Journal = {Journal of {P}hysiology},
Keywords = {area-v1, bicv-motion, bicv-sparse},
Month = mar,
Number = {1},
Pages = {215--243},
Pmcid = {PMC1557912},
Pmid = {4966457},
Priority = {0},
Title = {Receptive fields and functional architecture of monkey striate cortex.},
Url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557912/},
Volume = {195},
Year = {1968},
Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557912/}}
@article{Attneave54,
Author = {Attneave, F.},
Citeulike-Article-Id = {927122},
Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/13167245},
Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/13167245},
Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=13167245},
Date-Added = {2014-06-27 14:05:43},
Issn = {0033-295X},
Journal = {Psychological {R}eview},
Keywords = {bicv-sparse, perception, vision},
Month = may,
Number = {3},
Pages = {183--93},
Pmid = {13167245},
Priority = {2},
Title = {Some informational aspects of visual perception.},
Url = {http://view.ncbi.nlm.nih.gov/pubmed/13167245},
Volume = {61},
Year = {1954},
Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/13167245}}
@article{Dumoulin14,
Abstract = {Neurons in the visual cortex process a local region of visual space, but in order to adequately analyze natural images, neurons need to interact. The notion of an '' association field'' proposes that neurons interact to extract extended contours. Here, we identify the site and properties of contour integration mechanisms. We used functional magnetic resonance imaging ({fMRI}) and population receptive field ({pRF}) analyses. We devised {pRF} mapping stimuli consisting of contours. We isolated the contribution of contour integration mechanisms to the {pRF} by manipulating the contour content. This stimulus manipulation led to systematic changes in {pRF} size. Whereas a bank of Gabor filters quantitatively explains {pRF} size changes in V1, only {V2/V3} {pRF} sizes match the predictions of the association field. {pRF} size changes in later visual field maps, {hV4}, {LO}-1, and {LO}-2 do not follow either prediction and are probably driven by distinct classical receptive field properties or other extraclassical integration mechanisms. These {pRF} changes do not follow conventional {fMRI} signal strength measures. Therefore, analyses of {pRF} changes provide a novel computational neuroimaging approach to investigating neural interactions. We interpreted these results as evidence for neural interactions along co-oriented, cocircular receptive fields in the early extrastriate visual cortex ({V2/V3}), consistent with the notion of a contour association field.},
Author = {Dumoulin, Serge O. and Hess, Robert F. and May, Keith A. and Harvey, Ben M. and Rokers, Bas and Barendregt, Martijn},
Citeulike-Article-Id = {13243184},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1167/14.5.18},
Citeulike-Linkout-1 = {http://www.journalofvision.org/content/14/5/18.abstract},
Citeulike-Linkout-2 = {http://www.journalofvision.org/content/14/5/18.full.pdf},
Citeulike-Linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/24879865},
Citeulike-Linkout-4 = {http://www.hubmed.org/display.cgi?uids=24879865},
Date-Added = {2014-06-26 12:05:27},
Day = {30},
Doi = {10.1167/14.5.18},
Issn = {1534-7362},
Journal = {Journal of Vision},
Keywords = {assofield, bicv-sparse, contour, natural-scenes},
Month = may,
Number = {5},
Pages = {18+},
Pmid = {24879865},
Priority = {3},
Publisher = {Association for Research in Vision and Ophthalmology},
Title = {Contour extracting networks in early extrastriate cortex},
Url = {http://dx.doi.org/10.1167/14.5.18},
Volume = {14},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1167/14.5.18}}
@article{Aharon06,
Author = {Aharon, Michal and Elad, Michael and Bruckstein, Alfred M.},
Citeulike-Article-Id = {13242124},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.laa.2005.06.035},
Date-Added = {2014-06-25 11:28:50},
Doi = {10.1016/j.laa.2005.06.035},
Issn = {00243795},
Journal = {Linear Algebra and its Applications},
Keywords = {bicv-sparse, sparse\_coding, sparse\_hebbian\_learning},
Month = jul,
Number = {1},
Pages = {48--67},
Priority = {0},
Title = {On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them},
Url = {http://dx.doi.org/10.1016/j.laa.2005.06.035},
Volume = {416},
Year = {2006},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.laa.2005.06.035}}
@book{Elad10,
Author = {Elad, Michael},
Citeulike-Article-Id = {10801356},
Citeulike-Linkout-0 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&\#38;path=ASIN/144197010X},
Citeulike-Linkout-1 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/144197010X},
Citeulike-Linkout-10 = {http://www.librarything.com/isbn/144197010X},
Citeulike-Linkout-2 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&path=ASIN/144197010X},
Citeulike-Linkout-3 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&path=ASIN/144197010X},
Citeulike-Linkout-4 = {http://www.amazon.jp/exec/obidos/ASIN/144197010X},
Citeulike-Linkout-5 = {http://www.amazon.co.uk/exec/obidos/ASIN/144197010X/citeulike00-21},
Citeulike-Linkout-6 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/144197010X},
Citeulike-Linkout-7 = {http://www.worldcat.org/isbn/144197010X},
Citeulike-Linkout-8 = {http://books.google.com/books?vid=ISBN144197010X},
Citeulike-Linkout-9 = {http://www.amazon.com/gp/search?keywords=144197010X\&index=books\&linkCode=qs},
Date-Added = {2014-06-11 14:55:50},
Day = {19},
Edition = {1st Edition.},
Howpublished = {Hardcover},
Isbn = {144197010X},
Keywords = {bicv-sparse, computer-vision, sparse, sparse\_coding},
Month = aug,
Priority = {2},
Publisher = {Springer},
Title = {Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing},
Url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/144197010X},
Year = {2010},
Bdsk-Url-1 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20%5C&path=ASIN/144197010X}}
@article{Baudot13,
Author = {Baudot, Pierre and Levy, Manuel and Marre, Olivier and Monier, Cyril and Pananceau, Marc and Fr\'{e}gnac, Yves},
Citeulike-Article-Id = {13209969},
Citeulike-Linkout-0 = {http://dx.doi.org/10.3389/fncir.2013.00206},
Date-Added = {2014-06-04 11:24:37},
Doi = {10.3389/fncir.2013.00206},
Journal = {Frontiers in Neural Circuits},
Keywords = {area-v1, bicv-sparse, natural-scenes, taouali14},
Priority = {0},
Title = {Animation of natural scene by virtual eye-movements evokes high precision and low noise in {V1} neurons},
Url = {http://dx.doi.org/10.3389/fncir.2013.00206},
Volume = {7},
Year = {2013},
Bdsk-Url-1 = {http://dx.doi.org/10.3389/fncir.2013.00206}}
@article{DeWeese03,
Abstract = {Neurons are often assumed to operate in a highly unreliable manner: a neuron can signal the same stimulus with a variable number of action potentials. However, much of the experimental evidence supporting this view was obtained in the visual cortex. We have, therefore, assessed trial-to-trial variability in the auditory cortex of the rat. To ensure single-unit isolation, we used cell-attached recording. Tone-evoked responses were usually transient, often consisting of, on average, only a single spike per stimulus. Surprisingly, the majority of responses were not just transient, but were also binary, consisting of 0 or 1 action potentials, but not more, in response to each stimulus; several dramatic examples consisted of exactly one spike on 100\% of trials, with no trial-to-trial variability in spike count. The variability of such binary responses differs from comparably transient responses recorded in visual cortical areas such as area {MT}, and represent the lowest trial-to-trial variability mathematically possible for responses of a given firing rate. Our study thus establishes for the first time that transient responses in auditory cortex can be described as a binary process, rather than as a highly variable Poisson process. These results demonstrate that cortical architecture can support a more precise control of spike number than was previously recognized, and they suggest a re-evaluation of models of cortical processing that assume noisiness to be an inevitable feature of cortical codes.},
Address = {Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA.},
Author = {DeWeese, Michael R. and Wehr, Michael and Zador, Anthony M.},
Citeulike-Article-Id = {13209942},
Citeulike-Linkout-0 = {http://www.jneurosci.org/content/23/21/7940.abstract},
Date-Added = {2014-06-04 11:24:36},
Day = {27},
Journal = {Journal of Neuroscience},
Keywords = {bicv-sparse, taouali14},
Month = aug,
Number = {21},
Pages = {7940--7949},
Priority = {0},
Publisher = {Society for Neuroscience},
Title = {Binary Spiking in Auditory Cortex},
Url = {http://www.jneurosci.org/content/23/21/7940.abstract},
Volume = {23},
Year = {2003},
Bdsk-Url-1 = {http://www.jneurosci.org/content/23/21/7940.abstract}}
@article{Froudarakis14,
Author = {Froudarakis, Emmanouil and Berens, Philipp and Ecker, Alexander S. and Cotton, R. James and Sinz, Fabian H. and Yatsenko, Dimitri and Saggau, Peter and Bethge, Matthias and Tolias, Andreas S.},
Citeulike-Article-Id = {13145157},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nn.3707},
Citeulike-Linkout-1 = {http://dx.doi.org/10.1038/nn.3707},
Date-Added = {2014-06-02 21:35:03},
Day = {20},
Doi = {10.1038/nn.3707},
Issn = {1546-1726},
Journal = {Nature Neuroscience},
Keywords = {area-v1, bicv-sparse, natural-scenes, population\_coding, sparse\_coding},
Month = apr,
Number = {6},
Pages = {851--857},
Priority = {0},
Publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
Title = {Population code in mouse {V1} facilitates readout of natural scenes through increased sparseness},
Url = {http://dx.doi.org/10.1038/nn.3707},
Volume = {17},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/nn.3707}}
@inproceedings{Wei12,
Author = {Wei, Xue-Xin and Stocker, Alan A.},
Booktitle = {NIPS},
Citeulike-Article-Id = {13203121},
Date-Added = {2014-05-28 11:15:58},
Editor = {Bartlett, Peter L. and Pereira, Fernando C. N. and Burges, Christopher J. C. and Bottou, L{\'{e}}on and Weinberger, Kilian Q.},
Keywords = {bicv-sparse, vacher14},
Pages = {1313--1321},
Priority = {0},
Title = {Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference},
Year = {2012}}
@article{Liu14,
Abstract = {The L0-norm constraint in sparse coding has the advantage of producing the same diversity of receptive field shapes as physiology data, but is difficult for analysis. It remains a challenging issue to understand how the diverse shapes of V1 simple cell receptive fields emerge in visual cortex. This paper presents a biologically plausible learning algorithm, named Hebbian-based mean shift, for this problem. The L0-norm constraint optimizes the number of basis functions rather than their coefficients. We report that the optimization procedure is essentially a 0--1 programming of the selection of basis functions. By assuming that the basis functions are independently selected from a basis set, we find the spatial distribution of input samples containing a special basis function has a star shape and peaks at this basis function. Thus, learning the basis functions for sparse coding with the L0-norm can be interpreted as mode detection where the basis functions are the modes of the kernel density estimate. We employ mean shift to detect modes and prove that the updating rule for the mean shift is Hebbian. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like basis functions.},
Author = {Liu, Jiqian and Jia, Yunde},
Booktitle = {Chinese Science Bulletin},
Citeulike-Article-Id = {12932963},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/s11434-013-0041-4},
Citeulike-Linkout-1 = {http://link.springer.com/article/10.1007/s11434-013-0041-4},
Date-Added = {2014-01-21 13:31:07},
Day = {14},
Doi = {10.1007/s11434-013-0041-4},
Issn = {1861-9541},
Journal = {Chinese Science Bulletin},
Keywords = {area-v1, bicv-sparse, perrinet10shl, sparse},
Month = jan,
Number = {4},
Pages = {452--458},
Priority = {0},
Publisher = {Springer Berlin Heidelberg},
Title = {Hebbian-based mean shift for learning the diverse shapes of {V1} simple cell receptive fields},
Url = {http://dx.doi.org/10.1007/s11434-013-0041-4},
Volume = {59},
Year = {2014},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/s11434-013-0041-4}}
@article{Li08,
Abstract = {In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.},
Address = {The Rockefeller University, New York, NY 10065, USA. liwu@bnu.edu.cn},
Author = {Li, Wu and Pi\"{e}ch, Valentin and Gilbert, Charles D.},
Citeulike-Article-Id = {2702176},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.neuron.2007.12.011},
Citeulike-Linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2409109/},
Citeulike-Linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18255036},
Citeulike-Linkout-3 = {http://www.hubmed.org/display.cgi?uids=18255036},
Citeulike-Linkout-4 = {http://www.sciencedirect.com/science/article/B6WSS-4RSB6BB-F/1/c688a1b32ba244b2d43096feaec24cd4},
Date-Added = {2014-01-06 13:27:32},
Day = {7},
Doi = {10.1016/j.neuron.2007.12.011},
Issn = {0896-6273},
Journal = {Neuron},
Keywords = {area-v1, assofield, bicv-sparse, grouping},
Month = feb,
Number = {3},
Pages = {442--451},
Pmcid = {PMC2409109},
Pmid = {18255036},
Priority = {5},
Title = {Learning to Link Visual Contours},
Url = {http://dx.doi.org/10.1016/j.neuron.2007.12.011},
Volume = {57},
Year = {2008},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2007.12.011}}
@article{McManus11,
Abstract = {The ability to derive meaning from complex sensory input requires the integration of information over space and time, as well as cognitive mechanisms to shape that integration. We studied these processes in the primary visual cortex (V1), where neurons are thought to integrate visual inputs along contours defined by an association field ({AF}). We recorded extracellularly from single cells in macaque V1 to map the {AF}, by using an optimization algorithm to find the contours that maximally activated individual cells. We combined the algorithm with a delayed-match-to-sample task, to test how the optimal contours might be molded by the monkey's expectation for particular cue shapes. We found that V1 neurons were selective for complex shapes, a property previously ascribed to higher cortical areas. Furthermore, the shape selectivity was reprogrammed by perceptual task: Over the whole network, the optimal modes of geometric selectivity shifted between distinct subsets of the {AF}, alternately representing different stimulus features known to predominate in natural scenes. Our results suggest a general model of cortical function, whereby horizontal connections provide a broad domain of potential associations, and top--down inputs dynamically gate these linkages to task switch the function of a network.},
Author = {McManus, J. N. J. and Li, Wu and Gilbert, Charles D.},
Citeulike-Article-Id = {10163860},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1073/pnas.1105855108},
Citeulike-Linkout-1 = {http://www.pnas.org/content/108/24/9739.abstract},
Citeulike-Linkout-2 = {http://www.pnas.org/content/108/24/9739.full.pdf},
Citeulike-Linkout-3 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116391/},
Citeulike-Linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/21571645},
Citeulike-Linkout-5 = {http://www.hubmed.org/display.cgi?uids=21571645},
Date-Added = {2014-01-06 13:11:08},
Day = {13},
Doi = {10.1073/pnas.1105855108},
Issn = {1091-6490},
Journal = {Proceedings of the National Academy of Sciences},
Keywords = {area-v1, association-field, assofield, bicv-sparse, receptive-field},
Month = may,
Number = {24},
Pages = {9739--9746},
Pmcid = {PMC3116391},
Pmid = {21571645},
Priority = {0},
Publisher = {National Academy of Sciences},
Title = {Adaptive shape processing in primary visual cortex},
Url = {http://dx.doi.org/10.1073/pnas.1105855108},
Volume = {108},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1105855108}}
@article{Fischer07,
Abstract = {Abstract--- {M}eanwhile biorthogonal wavelets got a very popu- lar image processing tool, alternative multiresolution transforms have been proposed for solving some of their drawbacks, namely the poor selectivity in orientation and the lack of translation in- variance due to the aliasing between subbands. {T}hese transforms are generally overcomplete and consequently offer huge degrees of freedom in their design. {A}t the same time their optimization get a challenging task. {W}e proposed here a log-{G}abor wavelet transform gathering the excellent mathematical properties of the {G}abor functions with a carefully construction to maintain the properties of the filters and to permit exact reconstruction. {T}wo major improvements are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. {A}nd second, all the frequency bands including the highest and lowest frequencies are uniformly covered so as exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). {T}he transform is optimized not only mathematically but it also follows as much as possible the knowledge on the receptive field of the simple cells of the {P}rimary {V}isual {C}ortex ({V}1) of primates and on the statistics of natural images. {C}ompared to the state of the art, the log-{G}abor wavelets show excellent behavior in their ability to segregate the image information (e.g. the contrast edges) from incoherent {G}aussian noise by hard thresholding and to code the image features through a reduced set of coefficients with large magnitude. {S}uch characteristics make the transform a promising tool for general image processing tasks.},
Annote = {Special issue on Image Perception.},
Author = {Fischer, Sylvain and Redondo, Rafael and Perrinet, Laurent U. and Crist{\'{o}}bal, Gabriel},
Citeulike-Article-Id = {12825723},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1155/2007/90727},
Date-Added = {2013-12-10 14:09:00},
Doi = {10.1155/2007/90727},
Issn = {1687-6180},
Journal = {EURASIP Journal on Advances in Signal Processing},
Keywords = {assofield, bicv-sparse, denoising, filters, high-pass, image, log-gabor, motion-clouds, oriented, perrinet11sfn, sanz12jnp, system, transforms, vacher14, vision, wavelet},
Number = {1},
Pages = {090727--122},
Priority = {0},
Publisher = {Hindawi Publishing Corp.},
Title = {Sparse approximation of images inspired from the functional architecture of the primary visual areas},
Url = {http://dx.doi.org/10.1155/2007/90727},
Volume = {2007},
Year = {2007},
Bdsk-Url-1 = {http://dx.doi.org/10.1155/2007/90727}}
@article{Spratling13bicy,
Abstract = {Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.},
Author = {Spratling, M. W.},
Booktitle = {Biological Cybernetics},
Citeulike-Article-Id = {12824553},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/s00422-013-0579-x},
Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/24306061},
Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=24306061},
Citeulike-Linkout-3 = {http://link.springer.com/article/10.1007/s00422-013-0579-x},
Date-Added = {2013-12-09 11:09:47},
Day = {4},
Doi = {10.1007/s00422-013-0579-x},
Issn = {1432-0770},
Journal = {Biological Cybernetics},
Keywords = {area-v1, bicv-sparse, classification, perrinet10shl, receptive\_field, sparse\_coding},
Month = dec,
Number = {1},
Pages = {61--73},
Pmid = {24306061},
Priority = {0},
Publisher = {Springer Berlin Heidelberg},
Title = {Classification using sparse representations: a biologically plausible approach},
Url = {http://dx.doi.org/10.1007/s00422-013-0579-x},
Volume = {108},
Year = {2013},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/s00422-013-0579-x}}
@inbook{Nava13,
Abstract = {In recent years, with the advent of High-resolution Computed Tomography ({HRCT}), there has been an increased interest for diagnosing Chronic Obstructive Pulmonary Disease ({COPD}), which is commonly presented as emphysema. Since low-attenuation areas in {HRCT} images describe different emphysema patterns, the discrimination problem should focus on the characterization of both local intensities and global spatial variations. We propose a novel texture-based classification framework using complex Gabor filters and local binary patterns. We also analyzed a set of global and local texture descriptors to characterize emphysema morphology. The results have shown the effectiveness of our proposal and that the combination of descriptors provides robust features that lead to an improvement in the classification rate.},
Address = {Berlin, Heidelberg},
Author = {Nava, Rodrigo and Marcos, J. Victor and Escalante-Ram\'{\i}rez, Boris and Crist\'{o}bal, Gabriel and Perrinet, Laurent U. and Est\'{e}par, Ra\'{u}l S. J.},
Booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
Chapter = {27},
Citeulike-Article-Id = {12783199},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/978-3-642-41827-3\_27},
Citeulike-Linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-642-41827-3\_27},
Date-Added = {2013-11-12 11:38:54},
Doi = {10.1007/978-3-642-41827-3\_27},
Editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and Ruiz-Shulcloper, Jos\'{e} and Sanniti di Baja, Gabriella},
Isbn = {978-3-642-41826-6},
Issn = {1611-3349},
Keywords = {bicv-sparse, sparse\_coding, texture, translational-science},
Pages = {214--221},
Priority = {0},
Publisher = {Springer Berlin Heidelberg},
Series = {Lecture Notes in Computer Science},
Title = {Advances in Texture Analysis for Emphysema Classification},
Url = {http://dx.doi.org/10.1007/978-3-642-41827-3\_27},
Volume = {8259},
Year = {2013},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-642-41827-3%5C_27}}
@article{Benoit10,
Abstract = {An efficient modeling of the processing occurring at retina level and in the V1 visual cortex has been proposed in [1,2]. The aim of the paper is to show the advantages of using such a modeling in order to develop efficient and fast bio-inspired modules for low level image processing. At the retina level, a spatio-temporal filtering ensures accurate structuring of video data (noise and illumination variation removal, static and dynamic contour enhancement). In the V1 cortex, a frequency and orientation based analysis is performed. The combined use of retina and V1 cortex modeling allows the development of low level image processing modules for contour enhancement, for moving contour extraction, for motion analysis and for motion event detection. Each module is described and its performances are evaluated. The retina model has been integrated into a real-time {C/C}++ optimized program which is also presented in this paper with the derived computer vision tools.},
Author = {Benoit, A. and Caplier, A. and Durette, B. and Herault, J.},
Citeulike-Article-Id = {6826908},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.cviu.2010.01.011},
Date-Added = {2013-10-15 12:03:13},
Day = {04},
Doi = {10.1016/j.cviu.2010.01.011},
Issn = {1077-3142},
Journal = {Computer Vision and Image Understanding},
Keywords = {area-v1, bicv, bicv-sparse, retina},
Month = jul,
Number = {7},
Pages = {758--773},
Priority = {0},
Title = {Using Human Visual System modeling for bio-inspired low level image processing},
Url = {http://dx.doi.org/10.1016/j.cviu.2010.01.011},
Volume = {114},
Year = {2010},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.cviu.2010.01.011}}
@article{Charles12,
Abstract = {The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm ({LCA}). Among the cost functions we examine are approximate l(p) norms (0 ≤ p ≤ 2), modified l(p)-norms, block-l1 norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted l1 algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.},
Author = {Charles, Adam S. and Garrigues, Pierre and Rozell, Christopher J.},
Citeulike-Article-Id = {12716102},
Citeulike-Linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.310.2639\&\#38;rep=rep1\&\#38;type=pdf},
Citeulike-Linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.310.2639\&\#38;rep=rep1\&\#38;type=pdf},
Citeulike-Linkout-2 = {http://dx.doi.org/10.1162/neco\_a\_00372},
Citeulike-Linkout-3 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3774880/},
Citeulike-Linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/22970876},
Citeulike-Linkout-5 = {http://www.hubmed.org/display.cgi?uids=22970876},
Date-Added = {2013-10-11 10:38:44},
Doi = {10.1162/neco\_a\_00372},
Issn = {1530-888X},
Journal = {Neural Computation},
Keywords = {bicv-sparse, sparse, sparse\_coding},
Month = dec,
Number = {12},
Pages = {3317--3339},
Pmcid = {PMC3774880},
Pmid = {22970876},
Priority = {0},
Title = {A Common Network Architecture Efficiently Implements a Variety of {Sparsity-Based} Inference Problems},
Url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.310.2639\&\#38;rep=rep1\&\#38;type=pdf},
Volume = {24},
Year = {2012},
Bdsk-Url-1 = {http://dx.doi.org/10.1162/neco%5C_a%5C_00372}}
@incollection{August01,
Abstract = {A Markov process model for contour curvature is introduced via a stochastic differential equation. We analyze the distribution of such curves, and show that its mode is the Euler spiral, a curve minimizing changes in curvature. To probabilistically enhance noisy and low contrast curve images (e.g., edge and line operator responses), we combine this curvature process with the curve indicator random field, which is a prior for ideal curve images. In particular, we provide an expression for a nonlinear, minimum mean square error filter that requires the solution of two elliptic partial differential equations. Initial computations are reported, highlighting how the filter is curvature-selective, even when curvature is absent in the input.},
Author = {August, Jonas and Zucker, StevenW},
Booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
Citeulike-Article-Id = {12631971},
Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/3-540-44745-8\_33},
Citeulike-Linkout-1 = {http://link.springer.com/chapter/10.1007/3-540-44745-8\_33},
Date-Added = {2013-09-18 09:32:42},
Doi = {10.1007/3-540-44745-8\_33},
Editor = {Figueiredo, M\'{a}rio and Zerubia, Josiane and Jain, AnilK},
Keywords = {assofield, bicv-sparse, curvature, edge\_co-occurrence, markov-chain},
Pages = {497--512},
Priority = {4},
Publisher = {Springer Berlin Heidelberg},
Series = {Lecture Notes in Computer Science},
Title = {A Markov Process Using Curvature for Filtering Curve Images},
Url = {http://dx.doi.org/10.1007/3-540-44745-8\_33},
Volume = {2134},
Year = {2001},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/3-540-44745-8%5C_33}}
@article{Serre10,
Abstract = {Neuroscience is beginning to inspire a new generation of seeing machines.},
Address = {New York, NY, USA},
Author = {Serre, Thomas and Poggio, Tomaso},
Citeulike-Article-Id = {9429637},
Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1831425},
Citeulike-Linkout-1 = {http://dx.doi.org/10.1145/1831407.1831425},
Date-Added = {2013-09-11 15:50:29},
Day = {1},
Doi = {10.1145/1831407.1831425},
Issn = {0001-0782},
Journal = {Communications of the ACM},
Keywords = {assofield, bicv-sparse, feedforward\_hierarchical\_model, hierarchical\_model},
Month = oct,
Number = {10},
Pages = {54--61},
Priority = {4},
Publisher = {ACM},
Title = {A neuromorphic approach to computer vision},
Url = {http://dx.doi.org/10.1145/1831407.1831425},
Volume = {53},
Year = {2010},
Bdsk-Url-1 = {http://dx.doi.org/10.1145/1831407.1831425}}
@article{Cotter99,
Abstract = {The problem of signal representation in terms of basis vectors from a large, over-complete, spanning dictionary has been the focus of much research. Achieving a succinct, or `sparse', representation is known as the problem of best basis representation. Methods are considered which seek to solve this problem by sequentially building up a basis set for the signal. Three distinct algorithm types have appeared in the literature which are here termed basic matching pursuit ({BMP}), order recursive matching pursuit ({ORMP}) and modified matching pursuit ({MMP}). The algorithms are first described and then their computation is closely examined. Modifications are made to each of the procedures which improve their computational efficiency. The complexity of each algorithm is considered in two contexts; one where the dictionary is variable (time-dependent) and the other where the dictionary is fixed (time-independent). Experimental results are presented which demonstrate that the {ORMP} method is the best procedure in terms of its ability to give the most compact signal representation, followed by {MMP} and then {BMP} which gives the poorest results. Finally, weighing the performance of each algorithm, its computational complexity and the type of dictionary available, recommendations are made as to which algorithm should be used for a given problem},
Author = {Cotter, S. F. and Rao, B. D. and Kreutz-Delgado, K. and Adler, J.},