connectomics motif.bib

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@article{Passingham2002,
abstract = {The human frontal cortex has been reported to be proportion-ally larger than in other primates. Magnetic resonance scans of humans, apes and monkeys now cast doubt on this idea.$\backslash$n$\backslash$nHistorically there has been a long search for specializations of the human brain that might account for our intellectual pre-eminence. It has often been claimed that our frontal lobes, and in particular the prefrontal cortex, are especially enlarged relative to other animals.},
author = {Passingham, Richard E},
doi = {10.1038/nn0302-190},
file = {:Users/jonas/Downloads/papers/nn0302-190.pdf:pdf},
issn = {10976256},
journal = {Nature neuroscience},
number = {3},
pages = {190--192},
pmid = {11865306},
title = {{The frontal cortex: does size matter?}},
volume = {5},
year = {2002}
}
@article{Dugas-Ford2012,
abstract = {The six-layered neocortex is a uniquely mammalian structure with evolutionary origins that remain in dispute. One long-standing hypothesis, based on similarities in neuronal connectivity, proposes that homologs of the layer 4 input and layer 5 output neurons of neocortex are present in the avian forebrain, where they contribute to specific nuclei rather than to layers. We devised a molecular test of this hypothesis based on layer-specific gene expression that is shared across rodent and carnivore neocortex. Our findings establish that the layer 4 input and the layer 5 output cell types are conserved across the amniotes, but are organized into very different architectures, forming nuclei in birds, cortical areas in reptiles, and cortical layers in mammals.},
author = {Dugas-Ford, Jennifer and Rowell, Joanna J and Ragsdale, Clifton W},
doi = {10.1073/pnas.1204773109},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Dugas-Ford, Rowell, Ragsdale - 2012 - Cell-type homologies and the origins of the neocortex.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Animals,Base Sequence,Biological Evolution,Birds,Birds: anatomy \& histology,Birds: genetics,DNA Primers,DNA Primers: genetics,DNA, Complementary,DNA, Complementary: genetics,Image Processing, Computer-Assisted,In Situ Hybridization, Fluorescence,Mammals,Mammals: anatomy \& histology,Mammals: genetics,Microscopy, Fluorescence,Molecular Sequence Data,Neocortex,Neocortex: cytology,Phylogeny,Polymerase Chain Reaction,Reptiles,Reptiles: anatomy \& histology,Reptiles: genetics,Sequence Analysis, DNA},
month = oct,
number = {42},
pages = {16974--9},
pmid = {23027930},
title = {{Cell-type homologies and the origins of the neocortex.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23027930},
volume = {109},
year = {2012}
}
@inproceedings{Kemp2006a,
author = {Kemp, Charles and Tenenbaum, JB and Griffiths, TL},
booktitle = {Twenty-first National Conference on Artificial Intelligence (AAAI-06)},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Kemp, Tenenbaum, Griffiths - 2006 - Learning systems of concepts with an infinite relational model.pdf:pdf},
keywords = {blockmodel,relational},
mendeley-tags = {blockmodel,relational},
title = {{Learning systems of concepts with an infinite relational model}},
url = {http://www.aaai.org/Papers/AAAI/2006/AAAI06-061.pdf},
year = {2006}
}
@article{Horton2005,
abstract = {This year, the field of neuroscience celebrates the 50th anniversary of Mountcastle's discovery of the cortical column. In this review, we summarize half a century of research and come to the disappointing realization that the column may have no function. Originally, it was described as a discrete structure, spanning the layers of the somatosensory cortex, which contains cells responsive to only a single modality, such as deep joint receptors or cutaneous receptors. Subsequently, examples of columns have been uncovered in numerous cortical areas, expanding the original concept to embrace a variety of different structures and principles. A "column" now refers to cells in any vertical cluster that share the same tuning for any given receptive field attribute. In striate cortex, for example, cells with the same eye preference are grouped into ocular dominance columns. Unaccountably, ocular dominance columns are present in some species, but not others. In principle, it should be possible to determine their function by searching for species differences in visual performance that correlate with their presence or absence. Unfortunately, this approach has been to no avail; no visual faculty has emerged that appears to require ocular dominance columns. Moreover, recent evidence has shown that the expression of ocular dominance columns can be highly variable among members of the same species, or even in different portions of the visual cortex in the same individual. These observations deal a fatal blow to the idea that ocular dominance columns serve a purpose. More broadly, the term "column" also denotes the periodic termination of anatomical projections within or between cortical areas. In many instances, periodic projections have a consistent relationship with some architectural feature, such as the cytochrome oxidase patches in V1 or the stripes in V2. These tissue compartments appear to divide cells with different receptive field properties into distinct processing streams. However, it is unclear what advantage, if any, is conveyed by this form of columnar segregation. Although the column is an attractive concept, it has failed as a unifying principle for understanding cortical function. Unravelling the organization of the cerebral cortex will require a painstaking description of the circuits, projections and response properties peculiar to cells in each of its various areas.},
author = {Horton, Jonathan C and Adams, Daniel L},
doi = {10.1098/rstb.2005.1623},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Horton, Adams - 2005 - The cortical column a structure without a function.pdf:pdf},
issn = {0962-8436},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
keywords = {Brain Mapping,Cerebral Cortex,Cerebral Cortex: anatomy \& histology,Cerebral Cortex: physiology,Electron Transport Complex IV,Electron Transport Complex IV: metabolism,Humans,Models,Neurological,Visual Pathways,Visual Pathways: cytology,Visual Pathways: physiology,Visual Perception,Visual Perception: physiology,cortex},
mendeley-tags = {cortex},
month = apr,
number = {1456},
pages = {837--62},
pmid = {15937015},
title = {{The cortical column: a structure without a function.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1569491\&tool=pmcentrez\&rendertype=abstract},
volume = {360},
year = {2005}
}
@book{Murphy2012,
address = {Cambridge},
author = {Murphy, Kevin P},
isbn = {978-0262018029},
publisher = {The MIT Press},
title = {{Machine Learning: A Probabilistic Perspective}},
year = {2012}
}
@article{Brown2009,
abstract = {A central tenet of neuroscience is that the precise patterns of connectivity among neurons in a given brain area underlie its function. However, assigning any aspect of perception or behavior to the wiring of local circuits has been challenging. Here, we review recent work in sensory neocortex that demonstrates the power of identifying specific cell types when investigating the functional organization of brain circuits. These studies indicate that knowing the identity of both the presynaptic and postsynaptic cell type is key when analyzing neocortical circuits. Furthermore, identifying the circuit organization of particular cell types in the neocortex allows the recording and manipulation of each cell type's activity and the direct testing of its functional role in perception and behavior.},
author = {Brown, Solange P and Hestrin, Shaul},
doi = {10.1016/j.conb.2009.07.011},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Brown, Hestrin - 2009 - Cell-type identity a key to unlocking the function of neocortical circuits.pdf:pdf},
issn = {1873-6882},
journal = {Current opinion in neurobiology},
keywords = {Animals,Nerve Net,Nerve Net: physiology,Neural Inhibition,Neural Inhibition: physiology,Neurons,Neurons: physiology,Somatosensory Cortex,Somatosensory Cortex: physiology,Synapses,Synapses: physiology,Synaptic Transmission,Synaptic Transmission: physiology},
month = aug,
number = {4},
pages = {415--21},
pmid = {19674891},
title = {{Cell-type identity: a key to unlocking the function of neocortical circuits.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2739254\&tool=pmcentrez\&rendertype=abstract},
volume = {19},
year = {2009}
}
@article{Neal2003,
author = {Neal, R.M.},
doi = {10.1214/aos/1056562461},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Neal - 2003 - Slice sampling.pdf:pdf},
issn = {0090-5364},
journal = {Annals of Statistics},
keywords = {mcmc,slice},
mendeley-tags = {mcmc,slice},
month = jun,
number = {3},
pages = {705--741},
publisher = {JSTOR},
title = {{Slice sampling}},
url = {http://projecteuclid.org/Dienst/getRecord?id=euclid.aos/1056562461/ http://www.jstor.org/stable/10.2307/3448413},
volume = {31},
year = {2003}
}
@article{Douglas2004,
abstract = {We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.},
author = {Douglas, Rodney J and Martin, Kevan a C},
doi = {10.1146/annurev.neuro.27.070203.144152},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Douglas, Martin - 2004 - Neuronal circuits of the neocortex.pdf:pdf},
issn = {0147-006X},
journal = {Annual review of neuroscience},
keywords = {Animals,Cell Size,Cell Size: physiology,Humans,Models,Neocortex,Neocortex: cytology,Neocortex: physiology,Nerve Net,Nerve Net: cytology,Nerve Net: physiology,Neural Inhibition,Neural Inhibition: physiology,Neural Pathways,Neural Pathways: cytology,Neural Pathways: physiology,Neurological,Neurons,Neurons: physiology,Synaptic Transmission,Synaptic Transmission: physiology,cortex},
mendeley-tags = {cortex},
month = jan,
pages = {419--51},
pmid = {15217339},
title = {{Neuronal circuits of the neocortex.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15217339},
volume = {27},
year = {2004}
}
@article{Masland2001,
abstract = {The retina, like many other central nervous system structures, contains a huge diversity of neuronal types. Mammalian retinas contain approximately 55 distinct cell types, each with a different function. The census of cell types is nearing completion, as the development of quantitative methods makes it possible to be reasonably confident that few additional types exist. Although much remains to be learned, the fundamental structural principles are now becoming clear. They give a bottom-up view of the strategies used in the retina's processing of visual information and suggest new questions for physiological experiments and modeling.},
author = {Masland, R H},
doi = {10.1038/nn0901-877},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Masland - 2001 - The fundamental plan of the retina.pdf:pdf},
issn = {1097-6256},
journal = {Nature neuroscience},
keywords = {Animals,Color Perception,Color Perception: physiology,Primates,Primates: physiology,Retina,Retina: cytology,Retina: physiology,Retinal Cone Photoreceptor Cells,Retinal Cone Photoreceptor Cells: physiology,Retinal Ganglion Cells,Retinal Ganglion Cells: classification,Retinal Ganglion Cells: physiology,Visual Pathways,Visual Pathways: physiology},
month = sep,
number = {9},
pages = {877--86},
pmid = {11528418},
title = {{The fundamental plan of the retina.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11528418},
volume = {4},
year = {2001}
}
@article{Seung2014,
author = {Seung, H. Sebastian and S\"{u}mb\"{u}l, Uygar},
doi = {10.1016/j.neuron.2014.08.054},
file = {:Users/jonas/Downloads/papers/1-s2.0-S0896627314007843-main.pdf:pdf},
issn = {08966273},
journal = {Neuron},
month = sep,
number = {6},
pages = {1262--1272},
title = {{Neuronal Cell Types and Connectivity: Lessons from the Retina}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0896627314007843},
volume = {83},
year = {2014}
}
@article{Chandrasekaran2012,
author = {Chandrasekaran, Venkat and Parrilo, Pablo a. and Willsky, Alan S.},
doi = {10.1137/100816900},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Chandrasekaran, Parrilo, Willsky - 2012 - Convex Graph Invariants.pdf:pdf},
issn = {0036-1445},
journal = {SIAM Review},
keywords = {05c25,05c75,10,100816900,1137,52a41,90c25,ams subject classifications,convex optimization,deconvolution,doi,graph,graph hypothesis testing,graph invariants,graph sampling,graphs,majorization,spectral invariants},
month = jan,
number = {3},
pages = {513--541},
title = {{Convex Graph Invariants}},
url = {http://epubs.siam.org/doi/abs/10.1137/100816900},
volume = {54},
year = {2012}
}
@article{White1986,
author = {White, JG and Southgate, E and Thomson, JN and BRenner, S},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/White et al. - 1986 - The Structure of the Nervous System of the Nematode Caenorhabditis elegans.pdf:pdf},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
number = {1165},
pages = {1--340},
title = {{The Structure of the Nervous System of the Nematode Caenorhabditis elegans}},
volume = {314},
year = {1986}
}
@article{Freund1998,
author = {Freund, T.F. and Buzs\'{a}ki, G.},
doi = {10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-I},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Freund, Buzs\'{a}ki - 1998 - Interneurons of the hippocampus.pdf:pdf},
issn = {10509631},
journal = {Hippocampus},
keywords = {cerebral cortex comes from,gaba,gabaergic cells,inhibition,inhibitory neurons,knowledge about the neuronal,much of our current,nonprinci-,nonpyramidal cells,only recent work,organization of the,pal cells,studies of principal neurons},
month = dec,
number = {4},
pages = {347--470},
title = {{Interneurons of the hippocampus}},
url = {http://doi.wiley.com/10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-I},
volume = {6},
year = {1998}
}
@misc{Meinertzhagen2013,
abstract = {"Many thanks, Eric: If you mean the positions of the somata in the medulla cortex I'm afraid the answer is no. This is because the cortex surrounds the neuropile and the cell bodies are some way distant from their synapses in the latter. It would have been a truly monumental task to trace the axon back to the soma for each of the cells we reconstructed. Sorry, maybe one day! Best wishes, and thanks for your interest"},
author = {Meinertzhagen, I A},
title = {{Personal Communication}},
year = {2013}
}
@article{Reid2012,
abstract = {"Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex" by Hubel and Wiesel (1962) reported several important discoveries: orientation columns, the distinct structures of simple and complex receptive fields, and binocular integration. But perhaps the paper's greatest influence came from the concept of functional architecture (the complex relationship between in vivo physiology and the spatial arrangement of neurons) and several models of functionally specific connectivity. They thus identified two distinct concepts, topographic specificity and functional specificity, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity. Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference. Hubel and Wiesel also saw the need for functional specificity at a finer scale in their model of thalamic inputs to simple cells, verified in the 1990s. The difficult but potentially more important question of functional specificity between cortical neurons is only now becoming tractable with new experimental techniques.},
author = {Reid, R Clay},
doi = {10.1016/j.neuron.2012.06.031},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Reid - 2012 - From functional architecture to functional connectomics.pdf:pdf},
issn = {1097-4199},
journal = {Neuron},
keywords = {Animals,Models, Neurological,Nerve Net,Nerve Net: physiology,Neurons,Neurons: physiology,Orientation,Orientation: physiology,Visual Cortex,Visual Cortex: physiology,Visual Pathways,Visual Pathways: physiology},
month = jul,
number = {2},
pages = {209--17},
pmid = {22841307},
title = {{From functional architecture to functional connectomics.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22841307},
volume = {75},
year = {2012}
}
@article{Neal2000,
author = {Neal, Radford M.},
doi = {10.2307/1390653},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Neal - 2000 - Markov Chain Sampling Methods for Dirichlet Process Mixture Models.pdf:pdf},
issn = {10618600},
journal = {Journal of Computational and Graphical Statistics},
keywords = {crp,mcmc,mixtures,nonparametric bayes},
mendeley-tags = {crp,mcmc,mixtures,nonparametric bayes},
month = jun,
number = {2},
pages = {249},
title = {{Markov Chain Sampling Methods for Dirichlet Process Mixture Models}},
url = {http://www.jstor.org/stable/1390653?origin=crossref},
volume = {9},
year = {2000}
}
@inproceedings{Ishiguro,
author = {Ishiguro, Katsuhiko and Iwata, Tomoharu and Tenenbaum, Joshua},
booktitle = {Advances in Neural Information Processing Systems 23},
editor = {Lafferty, J. and Williams, C. K. I. and Shawe-Taylor, J. and Zemel, R.S. and Culotta, A.},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Ishiguro, Iwata, Tenenbaum - 2010 - Dynamic Infinite Relational Model for Time-varying Relational Data Analysis.pdf:pdf},
pages = {1--9},
title = {{Dynamic Infinite Relational Model for Time-varying Relational Data Analysis}},
year = {2010}
}
@article{Stevenson2009a,
abstract = {Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies.},
author = {Stevenson, Ian H and Rebesco, James M and Hatsopoulos, Nicholas G and Haga, Zach and Miller, Lee E and K\"{o}rding, Konrad P},
doi = {10.1109/TNSRE.2008.2010471},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Stevenson et al. - 2009 - Bayesian inference of functional connectivity and network structure from spikes(2).pdf:pdf},
issn = {1558-0210},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
keywords = {Action Potentials,Action Potentials: physiology,Algorithms,Animals,Bayes Theorem,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Computer Simulation,Humans,Models,Nerve Net,Nerve Net: physiology,Neurological,Synaptic Transmission,Synaptic Transmission: physiology},
month = jun,
number = {3},
pages = {203--13},
pmid = {19273038},
title = {{Bayesian inference of functional connectivity and network structure from spikes.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3406607\&tool=pmcentrez\&rendertype=abstract},
volume = {17},
year = {2009}
}
@inproceedings{Wanga2012,
author = {Wang, Chong and Blei, David M},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {Bartlett, P. and Pereira, F.C.N. and Burges, C.J.C. and Bottou, L. and Weinberger, K.Q.},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Wang, Blei - 2012 - Truncation-free Stochastic Variational Inference for Bayesian Nonparametric Models.pdf:pdf},
title = {{Truncation-free Stochastic Variational Inference for Bayesian Nonparametric Models}},
year = {2012}
}
@article{Birmele2012,
author = {Birmel\'{e}, Etienne},
doi = {10.1214/12-EJS698},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Birmel\'{e} - 2012 - Detecting local network motifs.pdf:pdf},
issn = {1935-7524},
journal = {Electronic Journal of Statistics},
keywords = {and phrases,biolog-,network motif,poisson approximation},
pages = {908--933},
title = {{Detecting local network motifs}},
url = {http://projecteuclid.org/euclid.ejs/1337604769},
volume = {6},
year = {2012}
}
@misc{visual6502source,
booktitle = {2013},
title = {{Github: Visual6502}},
url = {https://github.com/trebonian/visual6502}
}
@inproceedings{Miller,
author = {Miller, Kurt T and Griffiths, Thomas L and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems 22},
editor = {Bengio, Y. and Schuurmans, D. and Lafferty, J. and Williams, C. K. I. and Culotta, A.},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Miller, Griffiths, Jordan - 2009 - Nonparametric Latent Feature Models for Link Prediction.pdf:pdf},
keywords = {blockmodel},
mendeley-tags = {blockmodel},
pages = {1--9},
title = {{Nonparametric Latent Feature Models for Link Prediction}},
year = {2009}
}
@article{Anandkumar2012,
archivePrefix = {arXiv},
arxivId = {arXiv:1210.7559v2},
author = {Anandkumar, Anima and Ge, Rong and Hsu, Daniel and Kakade, Sham M and Telgarsky, Matus},
eprint = {arXiv:1210.7559v2},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Anandkumar et al. - 2012 - Tensor Decompositions for Learning Latent Variable Models.pdf:pdf},
keywords = {spectral inference},
mendeley-tags = {spectral inference},
pages = {1--55},
title = {{Tensor Decompositions for Learning Latent Variable Models}},
year = {2012}
}
@article{Ho2012,
author = {Ho, Qirong and Parikh, Ankur P. and Xing, Eric P.},
doi = {10.1080/01621459.2012.682530},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Ho, Parikh, Xing - 2012 - A Multiscale Community Blockmodel for Network Exploration.pdf:pdf},
isbn = {0001409107},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {bayesian nonparametrics,blockmodel,gibbs sampler,hierarchical network analysis,latent space model},
mendeley-tags = {blockmodel},
month = sep,
number = {499},
pages = {916--934},
title = {{A Multiscale Community Blockmodel for Network Exploration}},
url = {http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.682530},
volume = {107},
year = {2012}
}
@article{Helmstaedter2013,
author = {Helmstaedter, Moritz and Briggman, Kevin L. and Turaga, Srinivas C. and Jain, Viren and Seung, H. Sebastian and Denk, Winfried},
doi = {10.1038/nature12346},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Helmstaedter et al. - 2013 - Connectomic reconstruction of the inner plexiform layer in the mouse retina.pdf:pdf},
issn = {0028-0836},
journal = {Nature},
month = aug,
number = {7461},
pages = {168--174},
publisher = {Nature Publishing Group},
title = {{Connectomic reconstruction of the inner plexiform layer in the mouse retina}},
url = {http://www.nature.com/doifinder/10.1038/nature12346},
volume = {500},
year = {2013}
}
@article{Masoudi-Nejad2012,
abstract = {In recent years, there has been a great interest in studying different aspects of complex networks in a range of fields. One important local property of networks is network motifs, recurrent and statistically significant sub-graphs or patterns, which assists researchers in the identification of functional units in the networks. Although network motifs may provide a deep insight into the network's functional abilities, their detection is computationally challenging. Therefore several algorithms have been introduced to resolve this computationally hard problem. These algorithms can be classified under various paradigms such as exact counting methods, sampling methods, pattern growth methods and so on. Here, the authors will give a review on computational aspects of major algorithms and enumerate their related benefits and drawbacks from an algorithmic perspective.},
author = {Masoudi-Nejad, a and Schreiber, F and Kashani, Z R M},
doi = {10.1049/iet-syb.2011.0011},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Masoudi-Nejad, Schreiber, Kashani - 2012 - Building blocks of biological networks a review on major network motif discovery algorithms.pdf:pdf},
issn = {1751-8849},
journal = {IET systems biology},
keywords = {Algorithms,Biological,Biopolymers,Biopolymers: metabolism,Computer Simulation,Models,Signal Transduction,Signal Transduction: physiology,Software,motif,review},
mendeley-tags = {motif,review},
month = oct,
number = {5},
pages = {164--74},
pmid = {23101871},
title = {{Building blocks of biological networks: a review on major network motif discovery algorithms.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23101871},
volume = {6},
year = {2012}
}
@article{Hoffman2013,
author = {Hoffman, Matthew D and Blei, David M and Wang, Chong and Paisley, John},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Hoffman et al. - 2013 - Stochastic Variational Inference.pdf:pdf},
journal = {Journal of Machine Learning Research},
pages = {1303--1347},
title = {{Stochastic Variational Inference}},
volume = {14},
year = {2013}
}
@article{Sincich2005,
abstract = {Primary and secondary visual cortex (V1 and V2) form the foundation of the cortical visual system. V1 transforms information received from the lateral geniculate nucleus (LGN) and distributes it to separate domains in V2 for transmission to higher visual areas. During the past 20 years, schemes for the functional organization of V1 and V2 have been based on a tripartite framework developed by Livingstone \& Hubel (1988) . Since then, new anatomical data have accumulated concerning V1's input, its internal circuitry, and its output to V2. These new data, along with physiological and imaging studies, now make it likely that the visual attributes of color, form, and motion are not neatly segregated by V1 into different stripe compartments in V2. Instead, there are just two main streams, originating from cytochrome oxidase patches and interpatches, that project to V2. Each stream is composed of a mixture of magno, parvo, and konio geniculate signals. Further studies are required to elucidate how the patches and interpatches differ in the output they convey to extrastriate cortex.},
author = {Sincich, Lawrence C and Horton, Jonathan C},
doi = {10.1146/annurev.neuro.28.061604.135731},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Sincich, Horton - 2005 - The circuitry of V1 and V2 integration of color, form, and motion.pdf:pdf},
issn = {0147-006X},
journal = {Annual review of neuroscience},
keywords = {Animals,Color Perception,Color Perception: physiology,Feedback,Form Perception,Form Perception: physiology,Humans,Motion,Neural Networks (Computer),Visual Cortex,Visual Cortex: anatomy \& histology,Visual Cortex: physiology,Visual Pathways,Visual Pathways: physiology},
month = jan,
pages = {303--26},
pmid = {16022598},
title = {{The circuitry of V1 and V2: integration of color, form, and motion.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16022598},
volume = {28},
year = {2005}
}
@inproceedings{Herlau2012,
author = {Herlau, Tue and Morup, Morten and Schmidt, Mikkel N. and Hansen, Lars Kai},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
doi = {10.1109/CIP.2012.6232913},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Herlau et al. - 2012 - Detecting hierarchical structure in networks.pdf:pdf},
isbn = {978-1-4673-1878-5},
month = may,
pages = {1--6},
publisher = {IEEE},
title = {{Detecting hierarchical structure in networks}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232913},
year = {2012}
}
@article{Lacroix,
abstract = {The classic view of metabolism as a collection of metabolic pathways is being questioned with the currently available possibility of studying whole networks. Novel ways of decomposing the network into modules and motifs that could be considered as the building blocks of a network are being suggested. In this work, we introduce a new definition of motif in the context of metabolic networks. Unlike in previous works on (other) biochemical networks, this definition is not based only on topological features. We propose instead to use an alternative definition based on the functional nature of the components that form the motif, which we call a reaction motif. After introducing a formal framework motivated by biological considerations, we present complexity results on the problem of searching for all occurrences of a reaction motif in a network and introduce an algorithm that is fast in practice in most situations. We then show an initial application to the study of pathway evolution. Finally, we give some general features of the observed number of occurrences in order to highlight some structural features of metabolic networks.},
author = {Lacroix, Vincent and Fernandes, Cristina G and Sagot, Marie-France},
doi = {10.1109/TCBB.2006.55},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Lacroix, Fernandes, Sagot - Unknown - Motif search in graphs application to metabolic networks.pdf:pdf},
issn = {1545-5963},
journal = {IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM},
keywords = {Algorithms,Automated,Automated: methods,Biological,Computer Simulation,Models,Pattern Recognition,Protein Interaction Mapping,Protein Interaction Mapping: methods,Proteome,Proteome: metabolism,Signal Transduction,Signal Transduction: physiology,motif},
mendeley-tags = {motif},
number = {4},
pages = {360--8},
pmid = {17085845},
title = {{Motif search in graphs: application to metabolic networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17085845},
volume = {3}
}
@article{Varshney2011,
abstract = {Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.},
author = {Varshney, Lav R and Chen, Beth L and Paniagua, Eric and Hall, David H and Chklovskii, Dmitri B},
doi = {10.1371/journal.pcbi.1001066},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Varshney et al. - 2011 - Structural properties of the Caenorhabditis elegans neuronal network.pdf:pdf},
issn = {1553-7358},
journal = {PLoS computational biology},
keywords = {Animals,Caenorhabditis elegans,Caenorhabditis elegans: anatomy \& histology,Caenorhabditis elegans: physiology,Computational Biology,Gap Junctions,Gap Junctions: physiology,Gap Junctions: ultrastructure,Interneurons,Interneurons: cytology,Interneurons: physiology,Mathematical Concepts,Models, Anatomic,Models, Neurological,Motor Neurons,Motor Neurons: cytology,Motor Neurons: physiology,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: physiology,Sensory Receptor Cells,Sensory Receptor Cells: cytology,Sensory Receptor Cells: physiology,Synapses,Synapses: physiology,Synapses: ultrastructure,Systems Biology},
month = jan,
number = {2},
pages = {e1001066},
pmid = {21304930},
title = {{Structural properties of the Caenorhabditis elegans neuronal network.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3033362\&tool=pmcentrez\&rendertype=abstract},
volume = {7},
year = {2011}
}
@article{Mountcastle1997,
abstract = {The modular organization of nervous systems is a widely documented principle of design for both vertebrate and invertebrate brains of which the columnar organization of the neocortex is an example. The classical cytoarchitectural areas of the neocortex are composed of smaller units, local neural circuits repeated iteratively within each area. Modules may vary in cell type and number, in internal and external connectivity, and in mode of neuronal processing between different large entities; within any single large entity they have a basic similarity of internal design and operation. Modules are most commonly grouped into entities by sets of dominating external connections. This unifying factor is most obvious for the heterotypical sensory and motor areas of the neocortex. Columnar defining factors in homotypical areas are generated, in part, within the cortex itself. The set of all modules composing such an entity may be fractionated into different modular subsets by different extrinsic connections. Linkages between them and subsets in other large entities form distributed systems. The neighborhood relations between connected subsets of modules in different entities result in nested distributed systems that serve distributed functions. A cortical area defined in classical cytoarchitectural terms may belong to more than one and sometimes to several distributed systems. Columns in cytoarchitectural areas located at some distance from one another, but with some common properties, may be linked by long-range, intracortical connections.},
author = {Mountcastle, V B},
issn = {0006-8950},
journal = {Brain : a journal of neurology},
keywords = {Animals,Brain Mapping,Cell Division,Cell Movement,Cerebral Cortex,Cerebral Cortex: cytology,Humans,Models, Neurological,Neurons,Neurons: cytology,Neurons: physiology},
month = apr,
pages = {701--22},
pmid = {9153131},
title = {{The columnar organization of the neocortex.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9153131},
volume = {120 ( Pt 4},
year = {1997}
}
@article{Hubert1985,
author = {Hubert, Lawrence and Arabie, Phipps},
doi = {10.1007/BF01908075},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Hubert, Arabie - 1985 - Comparing partitions.pdf:pdf},
issn = {0176-4268},
journal = {Journal of Classification},
keywords = {ari,consensus,measures of agreement,measures of association,rand index},
mendeley-tags = {ari,rand index},
month = dec,
number = {1},
pages = {193--218},
title = {{Comparing partitions}},
url = {http://link.springer.com/10.1007/BF01908075},
volume = {2},
year = {1985}
}
@article{Grillner2005,
abstract = {To understand the interface between global brain function and molecular neuroscience--that is, the microcircuit level--a major challenge. Such understanding is prerequisite if we are to account for neural function in cellular terms. Very few vertebrate microcircuits are yet understood because their analysis is demanding technically. In this review of the TINS Microcircuits Special Feature, we attempt to shed light on the problem by comparing the operation of four types of microcircuit, to identify common molecular and cellular components. Central pattern generator (CPG) networks underlying rhythmic movements and hippocampal microcircuits that generate gamma and theta rhythms are compared with the neocortical microcircuits used in cognitive tasks and a cerebellar network. The long-term goal is to identify the components of a molecular and synaptic tool kit for the design of different microcircuits.},
author = {Grillner, Sten and Markram, Henry and {De Schutter}, Erik and Silberberg, Gilad and LeBeau, Fiona E N},
doi = {10.1016/j.tins.2005.08.003},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Grillner et al. - 2005 - Microcircuits in action--from CPGs to neocortex.pdf:pdf},
issn = {0166-2236},
journal = {Trends in neurosciences},
keywords = {Animals,Hippocampus,Hippocampus: physiology,Humans,Models, Neurological,Movement,Movement: physiology,Neocortex,Neocortex: physiology,Nerve Net,Nerve Net: physiology,Neural Networks (Computer),Neural Pathways,Neural Pathways: physiology,Periodicity},
month = oct,
number = {10},
pages = {525--33},
pmid = {16118022},
title = {{Microcircuits in action--from CPGs to neocortex.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16118022},
volume = {28},
year = {2005}
}
@article{Chen2006,
abstract = {We pursue the hypothesis that neuronal placement in animals minimizes wiring costs for given functional constraints, as specified by synaptic connectivity. Using a newly compiled version of the Caenorhabditis elegans wiring diagram, we solve for the optimal layout of 279 nonpharyngeal neurons. In the optimal layout, most neurons are located close to their actual positions, suggesting that wiring minimization is an important factor. Yet some neurons exhibit strong deviations from "optimal" position. We propose that biological factors relating to axonal guidance and command neuron functions contribute to these deviations. We capture these factors by proposing a modified wiring cost function.},
author = {Chen, Beth L and Hall, David H and Chklovskii, Dmitri B},
doi = {10.1073/pnas.0506806103},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Chen, Hall, Chklovskii - 2006 - Wiring optimization can relate neuronal structure and function.pdf:pdf},
isbn = {0506806103},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Animals,Caenorhabditis elegans,Caenorhabditis elegans: cytology,Caenorhabditis elegans: physiology,Models, Neurological,Neurons,Neurons: cytology,Neurons: physiology,Neurons: ultrastructure,Synapses,Synapses: physiology},
month = mar,
number = {12},
pages = {4723--8},
pmid = {16537428},
title = {{Wiring optimization can relate neuronal structure and function.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1550972\&tool=pmcentrez\&rendertype=abstract},
volume = {103},
year = {2006}
}
@article{Zaghloul2006,
abstract = {Prosthetic devices may someday be used to treat lesions of the central nervous system. Similar to neural circuits, these prosthetic devices should adapt their properties over time, independent of external control. Here we describe an artificial retina, constructed in silicon using single-transistor synaptic primitives, with two forms of locally controlled adaptation: luminance adaptation and contrast gain control. Both forms of adaptation rely on local modulation of synaptic strength, thus meeting the criteria of internal control. Our device is the first to reproduce the responses of the four major ganglion cell types that drive visual cortex, producing 3600 spiking outputs in total. We demonstrate how the responses of our device's ganglion cells compare to those measured from the mammalian retina. Replicating the retina's synaptic organization in our chip made it possible to perform these computations using a hundred times less energy than a microprocessor-and to match the mammalian retina in size and weight. With this level of efficiency and autonomy, it is now possible to develop fully implantable intraocular prostheses.},
author = {Zaghloul, Kareem a and Boahen, Kwabena},
doi = {10.1088/1741-2560/3/4/002},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Zaghloul, Boahen - 2006 - A silicon retina that reproduces signals in the optic nerve.pdf:pdf},
issn = {1741-2560},
journal = {Journal of neural engineering},
keywords = {Algorithms,Electrophysiology,Models, Neurological,Nerve Fibers,Nerve Fibers: physiology,Optic Nerve,Optic Nerve: physiology,Prostheses and Implants,Retina,Retinal Ganglion Cells,Retinal Ganglion Cells: physiology,Semiconductors,Silicon,Thalamus,Thalamus: physiology,Visual Cortex,Visual Cortex: physiology},
month = dec,
number = {4},
pages = {257--67},
pmid = {17124329},
title = {{A silicon retina that reproduces signals in the optic nerve.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17124329},
volume = {3},
year = {2006}
}
@article{Lennie2005,
abstract = {We review how neurons in the principal pathway connecting the retina to the visual cortex represent information about the chromatic and spatial characteristics of the retinal image. Our examination focuses particularly on individual neurons: what are their visual properties, how might these properties arise, what do these properties tell us about visual signal transformations, and how might these properties be expressed in perception? Our discussion is inclined toward studies on old-world monkeys and where possible emphasizes quantitative work that has led to or illuminates models of visual signal processing.},
author = {Lennie, Peter and Movshon, J Anthony},
doi = {10.1364/JOSAA.22.002013},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Lennie, Movshon - 2005 - Coding of color and form in the geniculostriate visual pathway (invited review).pdf:pdf},
issn = {1084-7529},
journal = {Journal of the Optical Society of America A},
keywords = {Action Potentials,Action Potentials: physiology,Animals,Color Perception,Color Perception: physiology,Corpus Striatum,Corpus Striatum: physiology,Form Perception,Form Perception: physiology,Geniculate Bodies,Geniculate Bodies: physiology,Haplorhini,Humans,Information Storage and Retrieval,Information Storage and Retrieval: methods,Models,Neurological,Neurons,Neurons: physiology,Visual Pathways,Visual Pathways: physiology},
month = oct,
number = {10},
pages = {2013},
pmid = {16277273},
title = {{Coding of color and form in the geniculostriate visual pathway (invited review)}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16277273 http://www.opticsinfobase.org/abstract.cfm?URI=JOSAA-22-10-2013},
volume = {22},
year = {2005}
}
@article{Douglas1991,
author = {Douglas, RJ and Martin, KA},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Douglas, Martin - 1991 - A functional microcircuit for cat visual cortex.pdf:pdf},
journal = {The Journal of Physiology},
pages = {735--769},
title = {{A functional microcircuit for cat visual cortex.}},
url = {http://jp.physoc.org/content/440/1/735.short},
year = {1991}
}
@inproceedings{Miller2012,
address = {Lake Tahoe, NV},
author = {Miller, Jeffrey W and Harrison, Matthew T},
booktitle = {NIPS Workshop on Modern Nonparametric Machine Learning},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Miller, Harrison - 2012 - Posterior consistency for the number of components in a finite mixture.pdf:pdf},
keywords = {nonparametric bayes},
mendeley-tags = {nonparametric bayes},
number = {x},
pages = {1--5},
title = {{Posterior consistency for the number of components in a finite mixture}},
year = {2012}
}
@article{Zador2012,
abstract = {Connectivity determines the function of neural circuits. Historically, circuit mapping has usually been viewed as a problem of microscopy, but no current method can achieve high-throughput mapping of entire circuits with single neuron precision. Here we describe a novel approach to determining connectivity. We propose BOINC ("barcoding of individual neuronal connections"), a method for converting the problem of connectivity into a form that can be read out by high-throughput DNA sequencing. The appeal of using sequencing is that its scale--sequencing billions of nucleotides per day is now routine--is a natural match to the complexity of neural circuits. An inexpensive high-throughput technique for establishing circuit connectivity at single neuron resolution could transform neuroscience research.},
author = {Zador, Anthony M and Dubnau, Joshua and Oyibo, Hassana K and Zhan, Huiqing and Cao, Gang and Peikon, Ian D},
doi = {10.1371/journal.pbio.1001411},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Zador et al. - 2012 - Sequencing the connectome.pdf:pdf},
issn = {1545-7885},
journal = {PLoS biology},
keywords = {Animals,Brain Mapping,Brain Mapping: methods,Connectome,Humans,Neural Pathways,Neural Pathways: physiology,Neurons,Neurons: physiology,Sequence Analysis, DNA,Sequence Analysis, DNA: methods},
month = jan,
number = {10},
pages = {e1001411},
pmid = {23109909},
title = {{Sequencing the connectome.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3479097\&tool=pmcentrez\&rendertype=abstract},
volume = {10},
year = {2012}
}
@inproceedings{Xu2006,
author = {Xu, Zhao and Tresp, Volker and Yu, Kai and Kriegel, Hans-peter},
booktitle = {Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Xu et al. - 2006 - Infinite Hidden Relational Models.pdf:pdf},
keywords = {blockmodel,relational},
mendeley-tags = {blockmodel,relational},
title = {{Infinite Hidden Relational Models}},
year = {2006}
}
@article{Milo2002,
abstract = {Complex networks are studied across many fields of science. To uncover their structural design principles, we defined "network motifs," patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.},
author = {Milo, R and Shen-Orr, S and Itzkovitz, S and Kashtan, N and Chklovskii, D and Alon, U},
doi = {10.1126/science.298.5594.824},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Milo et al. - 2002 - Network motifs simple building blocks of complex networks.pdf:pdf},
issn = {1095-9203},
journal = {Science (New York, N.Y.)},
keywords = {Algorithms,Animals,Caenorhabditis elegans,Caenorhabditis elegans: anatomy \& histology,Caenorhabditis elegans: physiology,Electronics,Escherichia coli,Escherichia coli: genetics,Food Chain,Gene Expression Regulation,Internet,Nerve Net,Nerve Net: physiology,Neurons,Neurons: physiology,Saccharomyces cerevisiae,Saccharomyces cerevisiae: genetics,Synapses,Synapses: physiology,motif},
mendeley-tags = {motif},
month = oct,
number = {5594},
pages = {824--7},
pmid = {12399590},
title = {{Network motifs: simple building blocks of complex networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12399590},
volume = {298},
year = {2002}
}
@article{Matsen2012,
abstract = {When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally-derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this paper we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Among Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, while PAM only gives a solution for the pre-specified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, while the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.},
archivePrefix = {arXiv},
arxivId = {1205.6867},
author = {Matsen, Frederick a. and Gallagher, Aaron and McCoy, Connor},
eprint = {1205.6867},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Matsen, Gallagher, McCoy - 2012 - Minimizing the average distance to a closest leaf in a phylogenetic tree.pdf:pdf},
month = may,
pages = {2--3},
title = {{Minimizing the average distance to a closest leaf in a phylogenetic tree}},
url = {http://arxiv.org/abs/1205.6867},
year = {2012}
}
@article{Takemura2013,
author = {Takemura, Shin-ya and Bharioke, Arjun and Lu, Zhiyuan and Nern, Aljoscha and Vitaladevuni, Shiv and Rivlin, Patricia K. and Katz, William T. and Olbris, Donald J. and Plaza, Stephen M. and Winston, Philip and Zhao, Ting and Horne, Jane Anne and Fetter, Richard D. and Takemura, Satoko and Blazek, Katerina and Chang, Lei-Ann and Ogundeyi, Omotara and Saunders, Mathew a. and Shapiro, Victor and Sigmund, Christopher and Rubin, Gerald M. and Scheffer, Louis K. and Meinertzhagen, Ian a. and Chklovskii, Dmitri B.},
doi = {10.1038/nature12450},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Takemura et al. - 2013 - A visual motion detection circuit suggested by Drosophila connectomics.pdf:pdf},
issn = {0028-0836},
journal = {Nature},
month = aug,
number = {7461},
pages = {175--181},
publisher = {Nature Publishing Group},
title = {{A visual motion detection circuit suggested by Drosophila connectomics}},
url = {http://www.nature.com/doifinder/10.1038/nature12450},
volume = {500},
year = {2013}
}
@article{Guerra2011,
abstract = {In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a "benchmark," the test to automatically distinguish between pyramidal cells and interneurons, defining "ground truth" by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies.},
author = {Guerra, Luis and McGarry, Laura M and Robles, V\'{\i}ctor and Bielza, Concha and Larra\~{n}aga, Pedro and Yuste, Rafael},
doi = {10.1002/dneu.20809},
file = {:Users/jonas/Downloads/papers/20809\_ftp.pdf:pdf},
issn = {1932-846X},
journal = {Developmental neurobiology},
keywords = {Algorithms,Animals,Cell Shape,Cell Shape: physiology,Cerebral Cortex,Cerebral Cortex: cytology,Cerebral Cortex: physiology,Image Cytometry,Image Cytometry: methods,Interneurons,Interneurons: classification,Interneurons: cytology,Interneurons: physiology,Mice,Mice, Inbred C57BL,Organ Culture Techniques,Pyramidal Cells,Pyramidal Cells: cytology,Pyramidal Cells: physiology},
month = jan,
number = {1},
pages = {71--82},
pmid = {21154911},
title = {{Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3058840\&tool=pmcentrez\&rendertype=abstract},
volume = {71},
year = {2011}
}
@article{Fischbach1989,
author = {Fischbach, K.-F. and Dittrich, A.P.M.},
doi = {10.1007/BF00218858},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Fischbach, Dittrich - 1989 - The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure.pdf:pdf},
issn = {0302-766X},
journal = {Cell and Tissue Research},
keywords = {insecta,mutanto - drosophila melanogaster,optic lobe n e,s - cell types,u r o n},
month = dec,
number = {3},
title = {{The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure}},
url = {http://link.springer.com/10.1007/BF00218858},
volume = {258},
year = {1989}
}
@article{Fusco2007,
abstract = {MOTIVATION: Transcription networks, and other directed networks can be characterized by some topological observables (e.g. network motifs), that require a suitable randomized network ensemble, typically with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Monte Carlo developed by (Chen et al.). AVAILABILITY: The algorithm is available at http://wwwteor.mi.infn.it/bassetti/downloads.html},
author = {Fusco, D and Bassetti, B and Jona, P and Lagomarsino, M Cosentino},
doi = {10.1093/bioinformatics/btm454},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Fusco et al. - 2007 - DIA-MCIS an importance sampling network randomizer for network motif discovery and other topological observables i.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Biological,Computer Simulation,Data Interpretation,Models,Signal Transduction,Signal Transduction: physiology,Software,Statistical,Transcription Factors,Transcription Factors: metabolism,motif},
mendeley-tags = {motif},
month = dec,
number = {24},
pages = {3388--90},
pmid = {17901083},
title = {{DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17901083},
volume = {23},
year = {2007}
}
@article{Nowicki2001,
author = {Nowicki, Krzysztof and Snijders, Tom a. B},
doi = {10.1198/016214501753208735},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Nowicki, Snijders - 2001 - Estimation and Prediction for Stochastic Blockstructures.pdf:pdf},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {cluster analysis,colored graph,gibbs sampling,latent class model,mixture model,social network},
month = sep,
number = {455},
pages = {1077--1087},
title = {{Estimation and Prediction for Stochastic Blockstructures}},
url = {http://www.tandfonline.com/doi/abs/10.1198/016214501753208735},
volume = {96},
year = {2001}
}
@article{Mountcastle1957,
author = {Mountcastle, Vernon B},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Mountcastle - 1957 - Modality and topographic properties of single neurons of cat's somatic sensory cortex.pdf:pdf},
journal = {Journal of Neurophysiology},
pages = {408--434},
title = {{Modality and topographic properties of single neurons of cat's somatic sensory cortex}},
url = {http://jn.physiology.org/content/jn/20/4/408.full.pdf},
volume = {20},
year = {1957}
}
@article{Bumbarger2013,
abstract = {The relationship between neural circuit function and patterns of synaptic connectivity is poorly understood, in part due to a lack of comparative data for larger complete systems. We compare system-wide maps of synaptic connectivity generated from serial transmission electron microscopy for the pharyngeal nervous systems of two nematodes with divergent feeding behavior: the microbivore Caenorhabditis elegans and the predatory nematode Pristionchus pacificus. We uncover a massive rewiring in a complex system of identified neurons, all of which are homologous based on neurite anatomy and cell body position. Comparative graph theoretical analysis reveals a striking pattern of neuronal wiring with increased connectional complexity in the anterior pharynx correlating with tooth-like denticles, a morphological feature in the mouth of P. pacificus. We apply focused centrality methods to identify neurons I1 and I2 as candidates for regulating predatory feeding and predict substantial divergence in the function of pharyngeal glands.},
author = {Bumbarger, Daniel J and Riebesell, Metta and R\"{o}delsperger, Christian and Sommer, Ralf J},
doi = {10.1016/j.cell.2012.12.013},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Bumbarger et al. - 2013 - System-wide rewiring underlies behavioral differences in predatory and bacterial-feeding nematodes.pdf:pdf},
issn = {1097-4172},
journal = {Cell},
keywords = {Animals,Caenorhabditis elegans,Caenorhabditis elegans: anatomy \& histology,Caenorhabditis elegans: physiology,Feeding Behavior,Interneurons,Interneurons: cytology,Interneurons: physiology,Motor Neurons,Motor Neurons: cytology,Motor Neurons: physiology,Nematoda,Nematoda: anatomy \& histology,Nematoda: physiology,Nerve Net,Neurons,Neurons: physiology,Pharynx,Pharynx: innervation,Pharynx: physiology,Predatory Behavior,Synapses,Synapses: physiology},
month = jan,
number = {1-2},
pages = {109--19},
pmid = {23332749},
title = {{System-wide rewiring underlies behavioral differences in predatory and bacterial-feeding nematodes.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23332749},
volume = {152},
year = {2013}
}
@article{Friston2011,
abstract = {Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.},
author = {Friston, Karl J},
doi = {10.1089/brain.2011.0008},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Friston - 2011 - Functional and effective connectivity a review.pdf:pdf},
issn = {2158-0022},
journal = {Brain connectivity},
keywords = {Animals,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Humans,Models, Neurological,Nerve Net,Nerve Net: physiology,Neural Pathways,Neural Pathways: physiology},
month = jan,
number = {1},
pages = {13--36},
pmid = {22432952},
title = {{Functional and effective connectivity: a review.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22432952},
volume = {1},
year = {2011}
}
@article{Chang2010,
author = {Chang, Jonathan and Blei, David M.},
doi = {10.1214/09-AOAS309},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Chang, Blei - 2010 - Hierarchical relational models for document networks.pdf:pdf},
issn = {1932-6157},
journal = {The Annals of Applied Statistics},
keywords = {relational},
mendeley-tags = {relational},
month = mar,
number = {1},
pages = {124--150},
title = {{Hierarchical relational models for document networks}},
url = {http://projecteuclid.org/euclid.aoas/1273584450},
volume = {4},
year = {2010}
}
@article{Salter-Townshend2013,
author = {Salter-Townshend, Michael and Murphy, Thomas Brendan},
doi = {10.1016/j.csda.2012.08.004},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Salter-Townshend, Murphy - 2013 - Variational Bayesian inference for the Latent Position Cluster Model for network data.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics \& Data Analysis},
keywords = {social network analysis},
month = jan,
number = {1},
pages = {661--671},
publisher = {Elsevier B.V.},
title = {{Variational Bayesian inference for the Latent Position Cluster Model for network data}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0167947312003076},
volume = {57},
year = {2013}
}
@article{Bartho2004,
abstract = {Most neuronal interactions in the cortex occur within local circuits. Because principal cells and GABAergic interneurons contribute differently to cortical operations, their experimental identification and separation is of utmost important. We used 64-site two-dimensional silicon probes for high-density recording of local neurons in layer 5 of the somatosensory and prefrontal cortices of the rat. Multiple-site monitoring of units allowed for the determination of their two-dimensional spatial position in the brain. Of the approximately 60,000 cell pairs recorded, 0.2\% showed robust short-term interactions. Units with significant, short-latency (<3 ms) peaks following their action potentials in their cross-correlograms were characterized as putative excitatory (pyramidal) cells. Units with significant suppression of spiking of their partners were regarded as putative GABAergic interneurons. A portion of the putative interneurons was reciprocally connected with pyramidal cells. Neurons physiologically identified as inhibitory and excitatory cells were used as templates for classification of all recorded neurons. Of the several parameters tested, the duration of the unfiltered (1 Hz to 5 kHz) spike provided the most reliable clustering of the population. High-density parallel recordings of neuronal activity, determination of their physical location and their classification into pyramidal and interneuron classes provide the necessary tools for local circuit analysis.},
author = {Barth\'{o}, Peter and Hirase, Hajime and Monconduit, Lena\"{\i}c and Zugaro, Michael and Harris, Kenneth D and Buzs\'{a}ki, Gy\"{o}rgy},
doi = {10.1152/jn.01170.2003},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Barth\'{o} et al. - 2004 - Characterization of neocortical principal cells and interneurons by network interactions and extracellular featu.pdf:pdf},
issn = {0022-3077},
journal = {Journal of neurophysiology},
keywords = {Action Potentials,Action Potentials: physiology,Animals,Extracellular Fluid,Extracellular Fluid: physiology,Interneurons,Interneurons: physiology,Neocortex,Neocortex: physiology,Nerve Net,Nerve Net: physiology,Neural Inhibition,Neural Inhibition: physiology,Rats,Rats, Sprague-Dawley},
month = jul,
number = {1},
pages = {600--8},
pmid = {15056678},
title = {{Characterization of neocortical principal cells and interneurons by network interactions and extracellular features.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15056678},
volume = {92},
year = {2004}
}
@article{Haeusler2009,
abstract = {The neocortex is a continuous sheet composed of rather stereotypical local microcircuits that consist of neurons on several laminae with characteristic synaptic connectivity patterns. An understanding of the structure and computational function of these cortical microcircuits may hold the key for understanding the enormous computational power of the neocortex. Two templates for the structure of laminar cortical microcircuits have recently been published by Thomson et al. and Binzegger et al., both resulting from long-lasting experimental studies (but based on different methods). We analyze and compare in this article the structure of these two microcircuit templates. In particular, we examine the distribution of network motifs, i.e. of subcircuits consisting of a small number of neurons. The distribution of these building blocks has recently emerged as a method for characterizing similarities and differences among complex networks. We show that the two microcircuit templates have quite different distributions of network motifs, although they both have a characteristic small-world property. In order to understand the dynamical and computational properties of these two microcircuit templates, we have generated computer models of them, consisting of Hodgkin-Huxley point neurons with conductance based synapses that have a biologically realistic short-term plasticity. The performance of these two cortical microcircuit models was studied for seven generic computational tasks that require accumulation and merging of information contained in two afferent spike inputs. Although the two models exhibit a different performance for some of these tasks, their average computational performance is very similar. When we changed the connectivity structure of these two microcircuit models in order to see which aspects of it are essential for computational performance, we found that the distribution of degrees of nodes is a common key factor for their computational performance. We also show that their computational performance is correlated with specific statistical properties of the circuit dynamics that is induced by a particular distribution of degrees of nodes.},
author = {Haeusler, Stefan and Schuch, Klaus and Maass, Wolfgang},
doi = {10.1016/j.jphysparis.2009.05.006},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Haeusler, Schuch, Maass - 2009 - Motif distribution, dynamical properties, and computational performance of two data-based cortical micr.pdf:pdf},
issn = {1769-7115},
journal = {Journal of physiology, Paris},
keywords = {Animals,Computer Simulation,Humans,Models, Neurological,Neocortex,Neocortex: physiology,Nerve Net,Nerve Net: physiology,Neural Networks (Computer),Nonlinear Dynamics},
number = {1-2},
pages = {73--87},
pmid = {19500669},
publisher = {Elsevier Ltd},
title = {{Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19500669},
volume = {103},
year = {2009}
}
@article{Blei2010a,
author = {Blei, David M. and Griffiths, Thomas L. and Jordan, Michael I.},
doi = {10.1145/1667053.1667056},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Blei, Griffiths, Jordan - 2010 - The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies.pdf:pdf},
issn = {00045411},
journal = {Journal of the ACM},
keywords = {Bayesian nonparametric statistics, unsupervised le},
month = jan,
number = {2},
pages = {1--30},
title = {{The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies}},
url = {http://portal.acm.org/citation.cfm?doid=1667053.1667056},
volume = {57},
year = {2010}
}
@article{Broderick2012a,
abstract = {In partitioning---a.k.a. clustering---data, we associate each data point with one and only one of some collection of groups called clusters or partition blocks. Here, we formally develop an analogous problem, called feature allocation, for associating data points with arbitrary non-negative integer numbers of groups, now called features or topics. We review known combinatorial stochastic process representations of clustering and develop analogous representations for the feature allocation case. We illustrate the clustering representations with examples that include the canonical nonparametric Bayesian clustering prior: the Chinese restaurant process or Dirichlet process. We not only illustrate the feature allocation representations with the canonical nonparametric Bayesian feature prior---the Indian buffet process or beta process---but also simultaneously discover new connections between the different representations for the Indian buffet process. We thereby bring the same level of completeness to the treatment of the Indian buffet that has previously been developed for the Chinese restaurant.},
archivePrefix = {arXiv},
arxivId = {1206.5862},
author = {Broderick, Tamara and Jordan, Michael I and Pitman, Jim},
eprint = {1206.5862},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Broderick, Jordan, Pitman - 2012 - Clusters and features from combinatorial stochastic processes.pdf:pdf},
month = jun,
pages = {39},
title = {{Clusters and features from combinatorial stochastic processes}},
url = {http://arxiv.org/abs/1206.5862},
year = {2012}
}
@article{Koboldt2013,
abstract = {Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. As technology has improved the cost and scale of genome characterization over sequencing's 40-year history, the scope of inquiry has commensurately broadened. Massively parallel sequencing has proven revolutionary, shifting the paradigm of genomics to address biological questions at a genome-wide scale. Sequencing now empowers clinical diagnostics and other aspects of medical care, including disease risk, therapeutic identification, and prenatal testing. This Review explores the current state of genomics in the massively parallel sequencing era.},
author = {Koboldt, Daniel C and Steinberg, Karyn Meltz and Larson, David E and Wilson, Richard K and Mardis, Elaine R},
doi = {10.1016/j.cell.2013.09.006},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Koboldt et al. - 2013 - The next-generation sequencing revolution and its impact on genomics.pdf:pdf},
issn = {1097-4172},
journal = {Cell},
month = sep,
number = {1},
pages = {27--38},
pmid = {24074859},
publisher = {Elsevier Inc.},
title = {{The next-generation sequencing revolution and its impact on genomics.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24074859},
volume = {155},
year = {2013}
}
@article{WattsStrogatz1998,
abstract = {Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.},
author = {Watts, D J and Strogatz, S H},
doi = {10.1038/30918},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Watts, Strogatz - 1998 - Collective dynamics of 'small-world' networks.pdf:pdf},
issn = {0028-0836},
journal = {Nature},
keywords = {Animals,Biological,Caenorhabditis elegans,Caenorhabditis elegans: physiology,Communicable Diseases,Communicable Diseases: transmission,Experimental,Games,Models,Nerve Net,Neurological,Theoretical},
month = jun,
number = {6684},
pages = {440--2},
pmid = {9623998},
title = {{Collective dynamics of 'small-world' networks.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/9623998},
volume = {393},
year = {1998}
}
@article{Ugander2013,
address = {New York, New York, USA},
author = {Ugander, Johan and Backstrom, Lars},
doi = {10.1145/2433396.2433461},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Ugander, Backstrom - 2013 - Balanced label propagation for partitioning massive graphs.pdf:pdf},
isbn = {9781450318693},
journal = {Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13},
keywords = {graph clustering,graph partitioning,graph theory,label propagation,so-},
mendeley-tags = {graph theory},
pages = {507},
publisher = {ACM Press},
title = {{Balanced label propagation for partitioning massive graphs}},
url = {http://dl.acm.org/citation.cfm?doid=2433396.2433461},
year = {2013}
}
@article{Druckmann2013,
abstract = {Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90\% accurate and manages to catch several human errors.},
author = {Druckmann, Shaul and Hill, Sean and Sch\"{u}rmann, Felix and Markram, Henry and Segev, Idan},
doi = {10.1093/cercor/bhs290},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Druckmann et al. - 2013 - A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analys.pdf:pdf},
issn = {1460-2199},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
keywords = {cell type,clustering,dimensionality reduction,gaba,supervised classi fi cation},
month = dec,
number = {12},
pages = {2994--3006},
pmid = {22989582},
title = {{A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22989582},
volume = {23},
year = {2013}
}
@inproceedings{James2010,
address = {New York, New York, USA},
author = {James, Greg and Silverman, Barry and Silverman, Brian},
booktitle = {ACM SIGGRAPH 2010 Talks on - SIGGRAPH '10},
doi = {10.1145/1837026.1837061},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/James, Silverman, Silverman - 2010 - Visualizing a classic CPU in action.pdf:pdf},
isbn = {9781450303941},
keywords = {computer history,integrated circuit,microprocessor,mos 6502,simulation,visualization},
pages = {1},
publisher = {ACM Press},
title = {{Visualizing a classic CPU in action}},
url = {http://portal.acm.org/citation.cfm?doid=1837026.1837061},
year = {2010}
}
@article{Hill2012,
abstract = {It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 ± 2.7\% (mean ± SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits.},
author = {Hill, Sean L and Wang, Yun and Riachi, Imad and Sch\"{u}rmann, Felix and Markram, Henry},
doi = {10.1073/pnas.1202128109},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Hill et al. - 2012 - Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neura.pdf:pdf},
issn = {1091-6490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Connectome,Connectome: methods,Models, Biological,Models, Statistical,Neocortex,Neocortex: cytology,Neocortex: physiology,Neural Pathways,Neural Pathways: cytology,Neural Pathways: physiology,Synapses,Synapses: physiology},
month = oct,
number = {42},
pages = {E2885--94},
pmid = {22991468},
title = {{Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3479474\&tool=pmcentrez\&rendertype=abstract},
volume = {109},
year = {2012}
}
@article{Girvan2002,
abstract = {A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.},
author = {Girvan, M and Newman, M E J},
doi = {10.1073/pnas.122653799},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Girvan, Newman - 2002 - Community structure in social and biological networks.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Algorithms,Animals,Community Networks,Computer Simulation,Humans,Models,Nerve Net,Nerve Net: physiology,Neural Networks (Computer),Social Behavior,Theoretical},
month = jun,
number = {12},
pages = {7821--6},
pmid = {12060727},
title = {{Community structure in social and biological networks.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=122977\&tool=pmcentrez\&rendertype=abstract},
volume = {99},
year = {2002}
}
@inproceedings{Ishiguro2012,
address = {La Palma, Canary Islands},
author = {Ishiguro, Katsuhiko},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
editor = {Lawrence, Neil and Girolami, Mark},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Ishiguro - 2012 - Subset Infinite Relational Models.pdf:pdf},
keywords = {relational},
mendeley-tags = {relational},
publisher = {JMLR WC\&P vol 22},
title = {{Subset Infinite Relational Models}},
volume = {XX},
year = {2012}
}
@article{Morgan2013,
author = {Morgan, Joshua L and Lichtman, Jeff W},
doi = {10.1038/nmeth.2480},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Morgan, Lichtman - 2013 - Why not connectomics.pdf:pdf},
issn = {1548-7091},
journal = {Nature Methods},
month = may,
number = {6},
pages = {494--500},
title = {{Why not connectomics?}},
url = {http://www.nature.com/doifinder/10.1038/nmeth.2480},
volume = {10},
year = {2013}
}
@article{Bock2011a,
abstract = {In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron's function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property--the preferred stimulus orientation--of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons' local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.},
author = {Bock, Davi D and Lee, Wei-Chung Allen and Kerlin, Aaron M and Andermann, Mark L and Hood, Greg and Wetzel, Arthur W and Yurgenson, Sergey and Soucy, Edward R and Kim, Hyon Suk and Reid, R Clay},
doi = {10.1038/nature09802},
file = {:Users/jonas/Library/Application Support/Mendeley Desktop/Downloaded/Bock et al. - 2011 - Network anatomy and in vivo physiology of visual cortical neurons.pdf:pdf},
issn = {1476-4687},
journal = {Nature},
keywords = {Animals,Calcium Signaling,Interneurons,Interneurons: physiology,Male,Mice,Microscopy, Electron, Transmission,Microscopy, Fluorescence,Microtomy,Nerve Net,Nerve Net: anatomy \& histology,Nerve Net: cytology,Nerve Net: physiology,Nerve Net: ultrastructure,Neural Inhibition,Neural Inhibition: physiology,Neurons,Neurons: physiology,Neurons: ultrastructure,Pyramidal Cells,Pyramidal Cells: physiology,Pyramidal Cells: ultrastructure,Synapses,Synapses: physiology,Visual Cortex,Visual Cortex: anatomy \& histology,Visual Cortex: cytology,Visual Cortex: physiology,Visual Cortex: ultrastructure},
month = mar,
number = {7337},
pages = {177--82},
pmid = {21390124},
publisher = {Nature Publishing Group},
title = {{Network anatomy and in vivo physiology of visual cortical neurons.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3095821\&tool=pmcentrez\&rendertype=abstract},
volume = {471},
year = {2011}
}