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@misc{zotero-4,
title = {{{LaTeX}}-Examples},
abstract = {LaTeX-examples - Examples for the usage of LaTeX},
howpublished = {https://github.com/MartinThoma/LaTeX-examples},
journal = {GitHub}
}
@article{Amit1997,
title = {Model of Global Spontaneous Activity and Local Structured Activity during Delay Periods in the Cerebral Cortex.},
volume = {7},
issn = {1047-3211, 1460-2199},
doi = {10.1093/cercor/7.3.237},
abstract = {We investigate self-sustaining stable states (attractors) in networks of integrate-and-fire neurons. First, we study the stability of spontaneous activity in an unstructured network. It is shown that the stochastic background activity, of 1-5 spikes/s, is unstable if all neurons are excitatory. On the other hand, spontaneous activity becomes self-stabilizing in presence of local inhibition, given reasonable values of the parameters of the network. Second, in a network sustaining physiological spontaneous rates, we study the effect of learning in a local module, expressed in synaptic modifications in specific populations of synapses. We find that if the average synaptic potentiation (LTP) is too low, no stimulus specific activity manifests itself in the delay period. Instead, following the presentation and removal of any stimulus there is, in the local module, a delay activity in which all neurons selective (responding visually) to any of the stimuli presented for learning have rates which gradually increase with the amplitude of synaptic potentiation. When the average LTP increases beyond a critical value, specific local attractors (stable states) appear abruptly against the background of the global uniform spontaneous attractor. In this case the local module has two available types of collective delay activity: if the stimulus is unfamiliar, the activity is spontaneous; if it is similar to a learned stimulus, delay activity is selective. These new attractors reflect the synaptic structure developed during learning. In each of them a small population of neurons have elevated rates, which depend on the strength of LTP. The remaining neurons of the module have their activity at spontaneous rates. The predictions made in this paper could be checked by single unit recordings in delayed response experiments.},
language = {en},
number = {3},
journal = {Cerebral Cortex},
author = {Amit, D. J. and Brunel, N.},
month = jan,
year = {1997},
pages = {237-252},
file = {articles/Amit1997.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/2FBK9UE2/237.html},
pmid = {9143444}
}
@article{Schaffer2013,
title = {A {{Complex}}-{{Valued Firing}}-{{Rate Model That Approximates}} the {{Dynamics}} of {{Spiking Networks}}},
volume = {9},
issn = {1553-7358},
doi = {10.1371/journal.pcbi.1003301},
language = {en},
number = {10},
journal = {PLoS Computational Biology},
author = {Schaffer, Evan S. and Ostojic, Srdjan and Abbott, L. F.},
editor = {Ermentrout, Bard},
month = oct,
year = {2013},
pages = {e1003301},
file = {articles/Schaffer2013.pdf}
}
@techreport{zotero-7,
title = {Easylist},
file = {manuals/latex/easylist.pdf},
note = {manuals/latex}
}
@unpublished{Depperschmidt2011,
title = {Markovketten},
author = {Depperschmidt, Andrej},
year = {2011},
file = {manuscripts/Depperschmidt2011_Markovketten.pdf}
}
@unpublished{Hoffmann2015h,
title = {Optimierung},
author = {Hoffmann},
year = {2015},
file = {manuscripts/Hoffmann2015_Optimierung.pdf}
}
@article{Kuljis2010,
title = {Integrative Understanding of Emergent Brain Properties, Quantum Brain Hypotheses, and Connectome Alterations in Dementia Are Key Challenges to Conquer {{Alzheimer}}'s Disease},
volume = {1},
doi = {10.3389/fneur.2010.00015},
abstract = {The biological substrate for cognition remains a challenge as much as defining this function of living beings. Here, we examine some of the difficulties to understand normal and disordered cognition in humans. We use aspects of Alzheimer's disease and related disorders to illustrate how the wealth of information at many conceptually separate, even intellectually decoupled, physical scales \textendash{} in particular at the Molecular Neuroscience versus Systems Neuroscience/Neuropsychology levels \textendash{} presents a challenge in terms of true interdisciplinary integration towards a coherent understanding. These unresolved dilemmas include critically the as yet untested quantum brain hypothesis, and the embryonic attempts to develop and define the so-called connectome in humans and in non-human models of disease. To mitigate these challenges, we propose a scheme incorporating the vast array of scales of the space and time (space\textendash{}time) manifold from at least the subatomic through cognitive-behavioral dimensions of inquiry, to achieve a new understanding of both normal and disordered cognition, that is essential for a new era of progress in the Generative Sciences and its application to translational efforts for disease prevention and treatment.},
journal = {Dementia},
author = {Kulji{\v s}, Rodrigo O.},
year = {2010},
keywords = {Alzheimer’s disease,Connectome,cognition,mesoscale,quantum brain},
pages = {15},
file = {articles/Kuljiš2010.pdf}
}
@article{Klausberger2008,
title = {Neuronal {{Diversity}} and {{Temporal Dynamics}}: {{The Unity}} of {{Hippocampal Circuit Operations}}},
volume = {321},
copyright = {American Association for the Advancement of Science},
issn = {0036-8075, 1095-9203},
shorttitle = {Neuronal {{Diversity}} and {{Temporal Dynamics}}},
doi = {10.1126/science.1149381},
abstract = {In the cerebral cortex, diverse types of neurons form intricate circuits and cooperate in time for the processing and storage of information. Recent advances reveal a spatiotemporal division of labor in cortical circuits, as exemplified in the CA1 hippocampal area. In particular, distinct GABAergic ($\gamma$-aminobutyric acid\textendash{}releasing) cell types subdivide the surface of pyramidal cells and act in discrete time windows, either on the same or on different subcellular compartments. They also interact with glutamatergic pyramidal cell inputs in a domain-specific manner and support synaptic temporal dynamics, network oscillations, selection of cell assemblies, and the implementation of brain states. The spatiotemporal specializations in cortical circuits reveal that cellular diversity and temporal dynamics coemerged during evolution, providing a basis for cognitive behavior.},
language = {en},
number = {5885},
journal = {Science},
author = {Klausberger, Thomas and Somogyi, Peter},
month = jul,
year = {2008},
pages = {53-57},
file = {articles/Klausberger2008.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/HQ6M4WI6/53.html},
pmid = {18599766}
}
@unpublished{Goette2009,
title = {Analysis {{I}}-{{III}}},
author = {Goette, Sebastian},
year = {2009},
file = {manuscripts/Goette2009_Analysis-I-III.pdf}
}
@techreport{zotero-13,
title = {The {{Docker Book}}},
file = {manuals/docker/the_docker_book.pdf},
note = {manuals/docker}
}
@article{Shadlen2013,
title = {Decision {{Making}} as a {{Window}} on {{Cognition}}},
volume = {80},
issn = {08966273},
doi = {10.1016/j.neuron.2013.10.047},
language = {en},
number = {3},
journal = {Neuron},
author = {Shadlen, Michael N. and Kiani, Roozbeh},
month = oct,
year = {2013},
pages = {791-806},
file = {articles/Shadlen2013.pdf}
}
@article{Barbour2007,
title = {What Can We Learn from Synaptic Weight Distributions?},
volume = {30},
issn = {01662236},
doi = {10.1016/j.tins.2007.09.005},
language = {en},
number = {12},
journal = {Trends in Neurosciences},
author = {Barbour, Boris and Brunel, Nicolas and Hakim, Vincent and Nadal, Jean-Pierre},
month = dec,
year = {2007},
pages = {622-629},
file = {articles/Barbour2007.pdf}
}
@techreport{zotero-16,
title = {Minted {{Source Code Highlighting}}},
file = {manuals/latex/minted_source_code_highlighting.pdf},
note = {manuals/latex}
}
@book{Hogg1978,
address = {New York},
edition = {4th ed},
title = {Introduction to Mathematical Statistics},
isbn = {978-0-02-355710-1},
lccn = {QA276 .H59 1978},
publisher = {{Macmillan}},
author = {Hogg, Robert V. and Craig, Allen T.},
year = {1978},
keywords = {Mathematical statistics},
file = {books/Hogg1978_Introduction-to-mathematical-statistics.pdf}
}
@misc{Hoffmann2014,
title = {Structural and Dynamical Aspects of Neural Networks with Anisotropic Tissue Geometry},
abstract = {Non-random connectivity has been repeatedly reported in cortical
networks, yet underlying connection principles of these patterns
remain elusive. Proposing an abstract geometric network model
reflecting stereotypical axonal and dendritic morphology of local
cortical layer 5 networks, we here investigate in how far anisotropy
in connectivity can constitute such an underlying connectivity
rule. Using a combination of analytical considerations and numerical
analysis, we find that while standard network measures and pair
connectivity remain unaffected, higher order connectivity is strongly
influenced by anisotropy, in many cases reflecting patterns found in
local cortical circuits. Presenting an abstract network model
featuring connectivity principles beyond distance-dependency, the
results shown here not only make a strong case for morphology-induced
rules as underlying connection principles of non-random patterns, but
may provide another step towards a network archetype greatly improving
upon the standard random model.},
author = {Hoffmann, Felix},
month = jun,
year = {2014},
file = {documents/Hoffmann2014.pdf}
}
@article{Miner2015,
title = {[{{Preprint}}] {{Plasticity}}-{{Driven Self}}-{{Organization}} under {{Topological Constraints Accounts}} for {{Non}}-{{Random Features}} of {{Cortical Synaptic Wiring}}.},
author = {Miner and Triesch},
year = {2015},
file = {articles/Miner2015.pdf}
}
@article{Dahlhaus1997,
title = {Identification of Synaptic Connections in Neural Ensembles by Graphical Models},
volume = {77},
issn = {0165-0270},
doi = {10.1016/S0165-0270(97)00100-3},
abstract = {A method for the identification of direct synaptic connections in a larger neural net is presented. It is based on a conditional correlation graph for multivariate point processes. The connections are identified via the partial spectral coherence of two neurons, given all others. It is shown how these coherences can be calculated by inversion of the spectral density matrix. In simulations with GENESIS, we discuss the relevance of the method for identifying different neural ensembles including an excitatory feedback loop and networks with lateral inhibitions.},
number = {1},
journal = {Journal of Neuroscience Methods},
author = {Dahlhaus, Rainer and Eichler, Michael and Sandk\"uhler, J\"urgen},
month = nov,
year = {1997},
keywords = {Graphical models,Multivariate point processes,Neuronal net,Partial spectral coherence,Synaptic connectivity},
pages = {93-107},
file = {articles/Dahlhaus1997.pdf}
}
@book{Mesbahi2010,
address = {Princeton},
series = {Princeton series in applied mathematics},
title = {Graph Theoretic Methods in Multiagent Networks},
isbn = {978-0-691-14061-2},
lccn = {T57.85 .M43 2010},
publisher = {{Princeton University Press}},
author = {Mesbahi, Mehran and Egerstedt, Magnus},
year = {2010},
keywords = {Graphic methods,Mathematical models,Multiagent systems,Network analysis (Planning)},
file = {books/Mesbahi2010_Graph-theoretic-methods-in-multiagent-networks.pdf}
}
@book{Seung2013,
address = {Boston},
edition = {First Mariner Books edition},
title = {Connectome: How the Brain's Wiring Makes Us Who We Are},
isbn = {978-0-547-67859-7},
lccn = {QP376 .S432 2013},
shorttitle = {Connectome},
publisher = {{Mariner Books, Houghton Mifflin Harcourt}},
author = {Seung, Sebastian},
year = {2013},
file = {books/Seung2013_Connectome-how-the-brain's-wiring-makes-us-who-we-are.epub}
}
@article{Borst2014,
title = {Fly Visual Course Control: Behaviour, Algorithms and Circuits},
volume = {15},
issn = {1471-003X, 1471-0048},
shorttitle = {Fly Visual Course Control},
doi = {10.1038/nrn3799},
number = {9},
journal = {Nature Reviews Neuroscience},
author = {Borst, Alexander},
month = aug,
year = {2014},
pages = {590-599},
file = {articles/Borst2014.pdf}
}
@article{Branco2010,
title = {Dendritic {{Discrimination}} of {{Temporal Input Sequences}} in {{Cortical Neurons}}},
volume = {329},
issn = {0036-8075, 1095-9203},
doi = {10.1126/science.1189664},
abstract = {The detection and discrimination of temporal sequences is fundamental to brain function and underlies perception, cognition, and motor output. By applying patterned, two-photon glutamate uncaging, we found that single dendrites of cortical pyramidal neurons exhibit sensitivity to the sequence of synaptic activation. This sensitivity is encoded by both local dendritic calcium signals and somatic depolarization, leading to sequence-selective spike output. The mechanism involves dendritic impedance gradients and nonlinear synaptic N-methyl-d-aspartate receptor activation and is generalizable to dendrites in different neuronal types. This enables discrimination of patterns delivered to a single dendrite, as well as patterns distributed randomly across the dendritic tree. Pyramidal cell dendrites can thus act as processing compartments for the detection of synaptic sequences, thereby implementing a fundamental cortical computation.},
language = {en},
number = {5999},
journal = {Science},
author = {Branco, Tiago and Clark, Beverley A. and H\"ausser, Michael},
month = sep,
year = {2010},
pages = {1671-1675},
file = {articles/Branco2010.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/2INIKHAX/1671.html},
pmid = {20705816}
}
@techreport{zotero-25,
title = {{{TikZ}} - {{A}} Brief {{Introduction}}},
file = {manuals/latex/tikz_-_a_brief_introduction.pdf},
note = {manuals/latex}
}
@article{Yang2013,
title = {Presynaptic Long-Term Plasticity},
volume = {5},
issn = {1663-3563},
doi = {10.3389/fnsyn.2013.00008},
abstract = {Long-term synaptic plasticity is a major cellular substrate for learning, memory, and behavioral adaptation. Although early examples of long-term synaptic plasticity described a mechanism by which postsynaptic signal transduction was potentiated, it is now apparent that there is a vast array of mechanisms for long-term synaptic plasticity that involve modifications to either or both the presynaptic terminal and postsynaptic site. In this article, we discuss current and evolving approaches to identify presynaptic mechanisms as well as discuss their limitations. We next provide examples of the diverse circuits in which presynaptic forms of long-term synaptic plasticity have been described and discuss the potential contribution this form of plasticity might add to circuit function. Finally, we examine the present evidence for the molecular pathways and cellular events underlying presynaptic long-term synaptic plasticity.},
journal = {Frontiers in Synaptic Neuroscience},
author = {Yang, Ying and Calakos, Nicole},
month = oct,
year = {2013},
file = {articles/Yang2013.pdf},
pmid = {24146648},
pmcid = {PMC3797957}
}
@techreport{zotero-27,
title = {Adjustbox {{Package}}},
file = {manuals/latex/adjustbox_package.pdf},
note = {manuals/latex}
}
@article{Hansel2013,
title = {Short-{{Term Plasticity Explains Irregular Persistent Activity}} in {{Working Memory Tasks}}},
volume = {33},
issn = {0270-6474, 1529-2401},
doi = {10.1523/JNEUROSCI.3455-12.2013},
language = {en},
number = {1},
journal = {Journal of Neuroscience},
author = {Hansel, D. and Mato, G.},
month = jan,
year = {2013},
pages = {133-149},
file = {articles/Hansel2013.pdf}
}
@article{Lefort2009,
title = {The {{Excitatory Neuronal Network}} of the {{C2 Barrel Column}} in {{Mouse Primary Somatosensory Cortex}}},
volume = {61},
issn = {0896-6273},
doi = {10.1016/j.neuron.2008.12.020},
abstract = {Summary
Local microcircuits within neocortical columns form key determinants of sensory processing. Here, we investigate the excitatory synaptic neuronal network of an anatomically defined cortical column, the C2 barrel column of mouse primary somatosensory cortex. This cortical column is known to process tactile information related to the C2 whisker. Through multiple simultaneous whole-cell recordings, we quantify connectivity maps between individual excitatory neurons located across all cortical layers of the C2 barrel column. Synaptic connectivity depended strongly upon somatic laminar location of both presynaptic and postsynaptic neurons, providing definitive evidence for layer-specific signaling pathways. The strongest excitatory influence upon the cortical column was provided by presynaptic layer 4 neurons. In all layers we found rare large-amplitude synaptic connections, which are likely to contribute strongly to reliable information processing. Our data set provides the first functional description of the excitatory synaptic wiring diagram of a physiologically relevant and anatomically well-defined cortical column at single-cell resolution.},
number = {2},
journal = {Neuron},
author = {Lefort, Sandrine and Tomm, Christian and Floyd Sarria, J. -C. and Petersen, Carl C. H.},
month = jan,
year = {2009},
keywords = {SYSNEURO},
pages = {301-316},
file = {articles/Lefort2009.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/8ZBRIFJ8/S0896627308010921.html}
}
@article{Mainen1996,
title = {Influence of Dendritic Structure on Firing Pattern in Model Neocortical Neurons},
volume = {382},
copyright = {\textcopyright{} 1996 Nature Publishing Group},
doi = {10.1038/382363a0},
language = {en},
number = {6589},
journal = {Nature},
author = {Mainen, Zachary F. and Sejnowski, Terrence J.},
month = jul,
year = {1996},
pages = {363-366},
file = {articles/Mainen1996.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/64K6STJ5/382363a0.html}
}
@article{Britten1992,
title = {The Analysis of Visual Motion: A Comparison of Neuronal and Psychophysical Performance},
volume = {12},
issn = {0270-6474, 1529-2401},
shorttitle = {The Analysis of Visual Motion},
abstract = {We compared the ability of psychophysical observers and single cortical neurons to discriminate weak motion signals in a stochastic visual display. All data were obtained from rhesus monkeys trained to perform a direction discrimination task near psychophysical threshold. The conditions for such a comparison were ideal in that both psychophysical and physiological data were obtained in the same animals, on the same sets of trials, and using the same visual display. In addition, the psychophysical task was tailored in each experiment to the physiological properties of the neuron under study; the visual display was matched to each neuron's preference for size, speed, and direction of motion. Under these conditions, the sensitivity of most MT neurons was very similar to the psychophysical sensitivity of the animal observers. In fact, the responses of single neurons typically provided a satisfactory account of both absolute psychophysical threshold and the shape of the psychometric function relating performance to the strength of the motion signal. Thus, psychophysical decisions in our task are likely to be based upon a relatively small number of neural signals. These signals could be carried by a small number of neurons if the responses of the pooled neurons are statistically independent. Alternatively, the signals may be carried by a much larger pool of neurons if their responses are partially intercorrelated.},
language = {en},
number = {12},
journal = {The Journal of Neuroscience},
author = {Britten, K. H. and Shadlen, M. N. and Newsome, W. T. and Movshon, J. A.},
month = jan,
year = {1992},
pages = {4745-4765},
file = {articles/Britten1992.pdf},
pmid = {1464765}
}
@article{Park2013,
title = {Structural and {{Functional Brain Networks}}: {{From Connections}} to {{Cognition}}},
volume = {342},
issn = {0036-8075, 1095-9203},
shorttitle = {Structural and {{Functional Brain Networks}}},
doi = {10.1126/science.1238411},
abstract = {How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.
Background The human brain presents a puzzling and challenging paradox: Despite a fixed anatomy, characterized by its connectivity, its functional repertoire is vast, enabling action, perception, and cognition. This contrasts with organs like the heart that have a dynamic anatomy but just one function. The resolution of this paradox may reside in the brain's network architecture, which organizes local interactions to cope with diverse environmental demands\textemdash{}ensuring adaptability, robustness, resilience to damage, efficient message passing, and diverse functionality from a fixed structure. This review asks how recent advances in understanding brain networks elucidate the brain's many-to-one (degenerate) function-structure relationships. In other words, how does diverse function arise from an apparently static neuronal architecture? We conclude that the emergence of dynamic functional connectivity, from static structural connections, calls for formal (computational) approaches to neuronal information processing that may resolve the dialectic between structure and function.
Schematic of the multiscale hierarchical organization of brain networks. Brain function or cognition can be described as the global integration of local (segregated) neuronal operations that underlies hierarchical message passing among cortical areas, and which is facilitated by hierarchical modular network architectures.
Advances Much of our understanding of brain connectivity rests on the way that it is measured and modeled. We consider two complementary approaches: the first has its basis in graph theory that aims to describe the network topology of (undirected) connections of the sort measured by noninvasive brain imaging of anatomical connections and functional connectivity (correlations) between remote sites. This is compared with model-based definitions of context-sensitive (directed) effective connectivity that are grounded in the biophysics of neuronal interactions. Recent topological network analyses of brain circuits suggest that modular and hierarchical structural networks are particularly suited for the functional integration of local (functionally specialized) neuronal operations that underlie cognition. Measurements of spontaneous activity reveal functional connectivity patterns that are similar to structural connectivity, suggesting that structural networks constrain functional networks. However, task-related responses that require context-sensitive integration disclose a divergence between function and structure that appears to rest mainly on long-range connections. In contrast to methods that describe network topology phenomenologically, model-based theoretical and computational approaches focus on the mechanisms of neuronal interactions that accommodate the dynamic reconfiguration of effective connectivity. We highlight the consilience between hierarchical topologies (based on structural and functional connectivity) and the effective connectivity that would be required for hierarchical message passing of the sort suggested by computational neuroscience.
Outlook In summary, neuronal interactions represent dynamics on a fixed structural connectivity that underlie cognition and behavior. Such divergence of function from structure is, perhaps, the most intriguing property of the brain and invites intensive future research. By studying the dynamics and self-organization of functional networks, we may gain insight into the true nature of the brain as the embodiment of the mind. The repertoire of functional networks rests upon the (hidden) structural architecture of connections that enables hierarchical functional integration. Understanding these networks will require theoretical models of neuronal processing that underlies cognition.},
language = {en},
number = {6158},
journal = {Science},
author = {Park, Hae-Jeong and Friston, Karl},
month = jan,
year = {2013},
pages = {1238411},
file = {articles/Park2013.pdf},
pmid = {24179229}
}
@book{Bollobas2001,
address = {Cambridge ; New York},
edition = {2nd edition},
title = {Random {{Graphs}}},
isbn = {978-0-521-80920-7},
language = {English},
publisher = {{Cambridge University Press}},
author = {Bollob\'as, B\'ela},
month = oct,
year = {2001},
file = {books/Bollobás2001_Random-Graphs.pdf}
}
@book{Klenke2006,
address = {Berlin},
edition = {Auflage: 1},
title = {{Wahrscheinlichkeitstheorie}},
isbn = {978-3-540-25545-1},
abstract = {Dieses Lehrbuch bietet eine umfassende moderne Einf\"uhrung in die wichtigsten Gebiete der Wahrscheinlichkeitstheorie und ihre ma\ss{}theoretischen Grundlagen. Themenschwerpunkte sind u.a.: Ma\ss{}- und Integrationstheorie, Grenzwerts\"atze f\"ur Summen von Zufallsvariablen, Martingale oder Perkolation. \"Uber 200 \"Ubungsaufgaben und zahlreiche Abbildungen runden die Darstellung ab. Breite und Auswahl der Themen sind einmalig in der deutschsprachigen Literatur.},
language = {Deutsch},
publisher = {{Springer}},
author = {Klenke, Achim},
year = {2006},
file = {books/Klenke2006_Wahrscheinlichkeitstheorie.pdf}
}
@article{Tannenbaum2016,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1605.03005},
primaryClass = {q-bio},
title = {Shaping Neural Circuits by High Order Synaptic Interactions},
abstract = {Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning.},
journal = {arXiv:1605.03005 [q-bio]},
author = {Tannenbaum, Neta Ravid and Burak, Yoram},
month = may,
year = {2016},
keywords = {Quantitative Biology - Neurons and Cognition},
file = {articles/Tannenbaum2016.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/25TVUBIS/1605.html}
}
@article{Butz2009,
title = {Activity-Dependent Structural Plasticity},
volume = {60},
issn = {01650173},
doi = {10.1016/j.brainresrev.2008.12.023},
language = {en},
number = {2},
journal = {Brain Research Reviews},
author = {Butz, Markus and W\"org\"otter, Florentin and {van Ooyen}, Arjen},
month = may,
year = {2009},
pages = {287-305},
file = {articles/Butz2009.pdf}
}
@article{Knoblauch2009,
title = {Memory {{Capacities}} for {{Synaptic}} and {{Structural Plasticity}}},
volume = {22},
issn = {0899-7667},
doi = {10.1162/neco.2009.08-07-588},
abstract = {Neural associative networks with plastic synapses have been proposed as computational models of brain functions and also for applications such as pattern recognition and information retrieval. To guide biological models and optimize technical applications, several definitions of memory capacity have been used to measure the efficiency of associative memory. Here we explain why the currently used performance measures bias the comparison between models and cannot serve as a theoretical benchmark. We introduce fair measures for information-theoretic capacity in associative memory that also provide a theoretical benchmark.},
number = {2},
journal = {Neural Computation},
author = {Knoblauch, Andreas and Palm, G\"unther and Sommer, Friedrich T.},
month = nov,
year = {2009},
pages = {289-341},
file = {articles/Knoblauch2009.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/QRIN68CX/neco.2009.html}
}
@article{Morrison2007,
title = {Spike-{{Timing}}-{{Dependent Plasticity}} in {{Balanced Random Networks}}},
volume = {19},
issn = {0899-7667},
doi = {10.1162/neco.2007.19.6.1437},
abstract = {The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP).},
number = {6},
journal = {Neural Computation},
author = {Morrison, Abigail and Aertsen, Ad and Diesmann, Markus},
month = apr,
year = {2007},
pages = {1437-1467},
file = {articles/Morrison2007.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/AN5ZM2ER/neco.2007.19.6.html}
}
@book{VanOoyen2003,
address = {Cambridge, Mass},
series = {Developmental cognitive neuroscience},
title = {Modeling Neural Development},
isbn = {978-0-262-22066-8},
lccn = {QP363.5 .M63 2003},
publisher = {{MIT Press}},
editor = {Van Ooyen, Arjen},
year = {2003},
keywords = {Developmental neurobiology,Methodology,Neural networks (Neurobiology)},
file = {books/Van Ooyen2003_Modeling-neural-development.pdf}
}
@book{Diestel2000,
address = {New York},
edition = {2nd edition},
title = {Graph {{Theory}}},
isbn = {978-0-387-95014-3},
language = {English},
publisher = {{Springer}},
author = {Diestel, Reinhard},
month = feb,
year = {2000},
file = {books/Diestel2000_Graph-Theory.pdf}
}
@article{Roxin2011,
title = {On the {{Distribution}} of {{Firing Rates}} in {{Networks}} of {{Cortical Neurons}}},
volume = {31},
issn = {0270-6474, 1529-2401},
doi = {10.1523/JNEUROSCI.1677-11.2011},
abstract = {The distribution of in vivo average firing rates within local cortical networks has been reported to be highly skewed and long tailed. The distribution of average single-cell inputs, conversely, is expected to be Gaussian by the central limit theorem. This raises the issue of how a skewed distribution of firing rates might result from a symmetric distribution of inputs. We argue that skewed rate distributions are a signature of the nonlinearity of the in vivo f\textendash{}I curve. During in vivo conditions, ongoing synaptic activity produces significant fluctuations in the membrane potential of neurons, resulting in an expansive nonlinearity of the f\textendash{}I curve for low and moderate inputs. Here, we investigate the effects of single-cell and network parameters on the shape of the f\textendash{}I curve and, by extension, on the distribution of firing rates in randomly connected networks.},
language = {en},
number = {45},
journal = {The Journal of Neuroscience},
author = {Roxin, Alex and Brunel, Nicolas and Hansel, David and Mongillo, Gianluigi and van Vreeswijk, Carl},
month = sep,
year = {2011},
pages = {16217-16226},
file = {articles/Roxin2011_2.pdf},
pmid = {22072673}
}
@techreport{zotero-42,
title = {Latexdiff},
file = {manuals/latex/latexdiff.pdf},
note = {manuals/latex}
}
@article{Hansel2012,
title = {The {{Mechanism}} of {{Orientation Selectivity}} in {{Primary Visual Cortex}} without a {{Functional Map}}},
volume = {32},
issn = {0270-6474, 1529-2401},
doi = {10.1523/JNEUROSCI.6284-11.2012},
abstract = {Neurons in primary visual cortex (V1) display substantial orientation selectivity even in species where V1 lacks an orientation map, such as in mice and rats. The mechanism underlying orientation selectivity in V1 with such a salt-and-pepper organization is unknown; it is unclear whether a connectivity that depends on feature similarity is required, or a random connectivity suffices. Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations. We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime. This is because in this regime the (large) untuned components in the excitatory and inhibitory contributions approximately cancel. As a result the untuned part of the input into a neuron as well as its modulation with orientation and time all have a size comparable to the neuronal threshold. However, the tuning of the F0 and F1 components of the input are uncorrelated and the high-frequency fluctuations are not tuned. This is reflected in the subthreshold voltage response. Remarkably, due to the nonlinear voltage-firing rate transfer function, the preferred orientation of the F0 and F1 components of the spike response are highly correlated.},
language = {en},
number = {12},
journal = {The Journal of Neuroscience},
author = {Hansel, David and van Vreeswijk, Carl},
month = mar,
year = {2012},
pages = {4049-4064},
file = {articles/Hansel2012.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/RBNXD9AB/4049.html},
pmid = {22442071}
}
@article{Fauth2016,
title = {Opposing {{Effects}} of {{Neuronal Activity}} on {{Structural Plasticity}}},
doi = {10.3389/fnana.2016.00075},
abstract = {The connectivity of the brain is continuously adjusted to new environmental influences by several activity-dependent adaptive processes. The most investigated adaptive mechanism is activity-dependent functional or synaptic plasticity regulating the transmission efficacy of existing synapses. Another important but less prominently discussed adaptive process is structural plasticity, which changes the connectivity by the formation and deletion of synapses. In this review, we show, based on experimental evidence, that structural plasticity can be classified similar to synaptic plasticity into two categories: (i) Hebbian structural plasticity, which leads to an increase (decrease) of the number of synapses during phases of high (low) neuronal activity and (ii) homeostatic structural plasticity, which balances these changes by removing and adding synapses. Furthermore, based on experimental and theoretical insights, we argue that each type of structural plasticity fulfills a different function. While Hebbian structural changes enhance memory lifetime, storage capacity, and memory robustness, homeostatic structural plasticity self-organizes the connectivity of the neural network to assure stability. However, the link between functional synaptic and structural plasticity as well as the detailed interactions between Hebbian and homeostatic structural plasticity are more complex. This implies even richer dynamics requiring further experimental and theoretical investigations.},
journal = {Frontiers in Neuroanatomy},
author = {Fauth, Michael and Tetzlaff, Christian},
year = {2016},
keywords = {architectural plasticity,network topology,structural plasticity,synaptic plasticity,timescales},
pages = {75},
file = {articles/Fauth2016.pdf}
}
@book{Rieke1997,
address = {Cambridge, Mass},
series = {Computational neuroscience},
title = {Spikes: Exploring the Neural Code},
isbn = {978-0-262-18174-7},
lccn = {QP364.5 .S66 1997},
shorttitle = {Spikes},
publisher = {{MIT Press}},
editor = {Rieke, Fred},
year = {1997},
keywords = {Neural transmission,Sensory neurons,low_qual},
file = {books/Rieke1997_Spikes-exploring-the-neural-code.pdf}
}
@book{Gabbiani2010,
address = {Amsterdam},
edition = {1. ed},
title = {Mathematics for Neuroscientists},
isbn = {978-0-12-374882-9},
language = {eng},
publisher = {{Elsevier, Acad. Press}},
author = {Gabbiani, Fabrizio and Cox, Steven},
year = {2010},
keywords = {Neurosciences Mathematics},
file = {books/Gabbiani2010_Mathematics-for-neuroscientists_2.pdf},
note = {OCLC: 837352028}
}
@article{Mongillo2010,
title = {Irregular {{Spiking}} and {{Multi}}-Stability in {{Recurrent Networks}} with {{Non}}-Linear {{Synaptic Transmission}}},
author = {Mongillo, Gianluigi and {van Vreeswijk}, Carl and Hansel, David},
year = {2010},
file = {articles/Mongillo2010.pdf}
}
@article{vanPelt2013,
title = {Estimating Neuronal Connectivity from Axonal and Dendritic Density Fields},
volume = {7},
issn = {1662-5188},
doi = {10.3389/fncom.2013.00160},
abstract = {Neurons innervate space by extending axonal and dendritic arborizations. When axons and dendrites come in close proximity of each other, synapses between neurons can be formed. Neurons vary greatly in their morphologies and synaptic connections with other neurons. The size and shape of the arborizations determine the way neurons innervate space. A neuron may therefore be characterized by the spatial distribution of its axonal and dendritic ``mass.'' A population mean ``mass'' density field of a particular neuron type can be obtained by averaging over the individual variations in neuron geometries. Connectivity in terms of candidate synaptic contacts between neurons can be determined directly on the basis of their arborizations but also indirectly on the basis of their density fields. To decide when a candidate synapse can be formed, we previously developed a criterion defining that axonal and dendritic line pieces should cross in 3D and have an orthogonal distance less than a threshold value. In this paper, we developed new methodology for applying this criterion to density fields. We show that estimates of the number of contacts between neuron pairs calculated from their density fields are fully consistent with the number of contacts calculated from the actual arborizations. However, the estimation of the connection probability and the expected number of contacts per connection cannot be calculated directly from density fields, because density fields do not carry anymore the correlative structure in the spatial distribution of synaptic contacts. Alternatively, these two connectivity measures can be estimated from the expected number of contacts by using empirical mapping functions. The neurons used for the validation studies were generated by our neuron simulator NETMORPH. An example is given of the estimation of average connectivity and Euclidean pre- and postsynaptic distance distributions in a network of neurons represented by their population mean density fields.},
journal = {Frontiers in Computational Neuroscience},
author = {{van Pelt}, Jaap and {van Ooyen}, Arjen},
month = nov,
year = {2013},
file = {articles/van Pelt2013.pdf},
pmid = {24324430},
pmcid = {PMC3839411}
}
@article{Hoffmann2016a,
title = {Non-Random Network Connectivity Comes in Pairs},
doi = {10.12751/nncn.bc2016.0079},
author = {Hoffmann, Felix Z. and Triesch, Jochen},
year = {2016},
keywords = {me,poster},
file = {articles/Hoffmann_BCCN2016.pdf},
note = {poster=BCCN}
}
@article{Okun2009,
title = {Balance of Excitation and Inhibition},
volume = {4},
issn = {1941-6016},
doi = {10.4249/scholarpedia.7467},
language = {en},
number = {8},
journal = {Scholarpedia},
author = {Okun, Michael and Lampl, Ilan},
year = {2009},
pages = {7467},
file = {articles/Okun2009.pdf}
}
@article{Kampa2006,
title = {Cortical Feed-Forward Networks for Binding Different Streams of Sensory Information},
volume = {9},
copyright = {\textcopyright{} 2006 Nature Publishing Group},
issn = {1097-6256},
doi = {10.1038/nn1798},
abstract = {Different streams of sensory information are transmitted to the cortex where they are merged into a percept in a process often termed 'binding.' Using recordings from triplets of rat cortical layer 2/3 and layer 5 pyramidal neurons, we show that specific subnetworks within layer 5 receive input from different layer 2/3 subnetworks. This cortical microarchitecture may represent a mechanism that enables the main output of the cortex (layer 5) to bind different features of a sensory stimulus.},
language = {en},
number = {12},
journal = {Nature Neuroscience},
author = {Kampa, Bj\"orn M. and Letzkus, Johannes J. and Stuart, Greg J.},
month = dec,
year = {2006},
pages = {1472-1473},
file = {articles/Kampa2006.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/4D22ZBS3/nn1798.html}
}
@article{Butz2014,
title = {Homeostatic Structural Plasticity Increases the Efficiency of Small-World Networks},
volume = {6},
issn = {1663-3563},
doi = {10.3389/fnsyn.2014.00007},
abstract = {In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization.},
journal = {Frontiers in Synaptic Neuroscience},
author = {Butz, Markus and Steenbuck, Ines D. and {van Ooyen}, Arjen},
month = apr,
year = {2014},
file = {articles/Butz2014.pdf},
pmid = {24744727},
pmcid = {PMC3978244}
}
@unpublished{Hoffmann2009f,
title = {Wahrscheinlichkeitstheorie},
author = {Hoffmann, Felix},
year = {2009},
file = {manuscripts/Hoffmann2009_Wahrscheinlichkeitstheorie.pdf}
}
@article{Hellwig1994,
title = {Synapses on Axon Collaterals of Pyramidal Cells Are Spaced at Random Intervals: A {{Golgi}} Study in the Mouse Cerebral Cortex},
volume = {71},
issn = {0340-1200, 1432-0770},
shorttitle = {Synapses on Axon Collaterals of Pyramidal Cells Are Spaced at Random Intervals},
doi = {10.1007/BF00198906},
abstract = {In this study we investigated the arrangement of synapses on local axon collaterals of Golgi-stained pyramidal neurons in the mouse cerebral cortex. As synaptic markers we considered axonal swellings visible at high magnification under the light microscope. Such axonal swellings coincide with synaptic boutons, as has been demonstrated in a number of combined light and electron microscopic studies. These studies also indicated that, in most cases, one bouton corresponds precisely to one synapse. Golgi-impregnated axonal trees of 20 neocortical pyramidal neurons were drawn with a camera lucida. Axonal swellings were marked on the drawings. Most swellings were `en passant'; occasionally, they were situated at the tip of short, spine-like processes. On axon collaterals, the average interval between swellings was 4.5 $\mu$m. On the axonal main stem, the swellings were always less densely packed than on the collaterals. Statistical analysis of the spatial distribution of the swellings did not reveal any special patterns. Instead, the arrangement of swellings on individual collaterals follows a Poisson distribution. Moreover, the same holds to a large extent for the entire collection of pyramidal cell collaterals. This suggests that a single Poisson process, characterized by only one rate parameter (number of synapses per unit length), describes most of the spatial distribution of synapses along pyramidal cell collaterals. These findings do not speak in favour of a pronounced target specificity of pyramidal neurons at the synaptic level. Instead, our results support a probabilistic model of cortical connectivity.},
language = {en},
number = {1},
journal = {Biological Cybernetics},
author = {Hellwig, Bernhard and Sch\"uz, Almut and Aertsen, Ad},
month = may,
year = {1994},
keywords = {Bioinformatics,Computer Appl. in Life Sciences,Neurobiology},
pages = {1-12},
file = {articles/Hellwig1994.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/75HB8WU3/BF00198906.html}
}
@article{Erdos1959,
title = {On Random Graphs, {{I}}},
volume = {6},
journal = {Publicationes Mathematicae (Debrecen)},
author = {Erd{\H o}s, P. and R\'enyi, A.},
year = {1959},
keywords = {graphs,random},
pages = {290-297},
file = {articles/Erd˝os1959.pdf}
}
@article{Nudo2013,
title = {Recovery after Brain Injury: Mechanisms and Principles},
volume = {7},
issn = {1662-5161},
shorttitle = {Recovery after Brain Injury},
doi = {10.3389/fnhum.2013.00887},
journal = {Frontiers in Human Neuroscience},
author = {Nudo, Randolph J.},
year = {2013},
file = {articles/Nudo2013.pdf}
}
@article{Hawkes1971a,
title = {Spectra of Some Self-Exciting and Mutually Exciting Point Processes},
volume = {58},
issn = {0006-3444, 1464-3510},
doi = {10.1093/biomet/58.1.83},
abstract = {SUMMARY In recent years methods of data analysis for point processes have received some attention, for example, by Cox \& Lewis (1966) and Lewis (1964). In particular Bartlett (1963a, b) has introduced methods of analysis based on the point spectrum. Theoretical models are relatively sparse. In this paper the theoretical properties of a class of processes with particular reference to the point spectrum or corresponding covariance density functions are discussed. A particular result is a self-exciting process with the same second-order properties as a certain doubly stochastic process. These are not distinguishable by methods of data analysis based on these properties.},
language = {en},
number = {1},
journal = {Biometrika},
author = {Hawkes, Alan G.},
month = jan,
year = {1971},
keywords = {Covariance density,Point process,Self-exciting point process,Spectrum of point process},
pages = {83-90},
file = {articles/Hawkes1971.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/MCS8DJQM/83.html}
}
@book{Mardia2000,
address = {Chichester ; New York},
series = {Wiley series in probability and statistics},
title = {Directional Statistics},
isbn = {978-0-471-95333-3},
lccn = {QA276 .J864 2000},
publisher = {{J. Wiley}},
author = {Mardia, K. V. and Jupp, Peter E.},
year = {2000},
keywords = {Distribution (Probability theory),Mathematical statistics,Sampling (Statistics)},
file = {books/Mardia2000_Directional-statistics2.pdf}
}
@unpublished{Hoffmann2009e,
title = {Ringe},
author = {Hoffmann, Felix},
year = {2009},
file = {manuscripts/Hoffmann2009_Ringe.pdf}
}
@article{Regehr2012,
title = {Short-{{Term Presynaptic Plasticity}}},
volume = {4},
issn = {, 1943-0264},
doi = {10.1101/cshperspect.a005702},
abstract = {Different types of synapses are specialized to interpret spike trains in their own way by virtue of the complement of short-term synaptic plasticity mechanisms they possess. Numerous types of short-term, use-dependent synaptic plasticity regulate neurotransmitter release. Short-term depression is prominent after a single conditioning stimulus and recovers in seconds. Sustained presynaptic activation can result in more profound depression that recovers more slowly. An enhancement of release known as facilitation is prominent after single conditioning stimuli and lasts for hundreds of milliseconds. Finally, tetanic activation can enhance synaptic strength for tens of seconds to minutes through processes known as augmentation and posttetantic potentiation. Progress in clarifying the properties, mechanisms, and functional roles of these forms of short-term plasticity is reviewed here.},
language = {en},
number = {7},
journal = {Cold Spring Harbor Perspectives in Biology},
author = {Regehr, Wade G.},
month = jan,
year = {2012},
pages = {a005702},
file = {articles/Regehr2012.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/FM3RNA9F/a005702.html},
pmid = {22751149}
}
@article{Vogels2011,
title = {Inhibitory {{Plasticity Balances Excitation}} and {{Inhibition}} in {{Sensory Pathways}} and {{Memory Networks}}},
volume = {334},
issn = {0036-8075, 1095-9203},
doi = {10.1126/science.1211095},
abstract = {Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.},
language = {en},
number = {6062},
journal = {Science},
author = {Vogels, T. P. and Sprekeler, H. and Zenke, F. and Clopath, C. and Gerstner, W.},
month = dec,
year = {2011},
pages = {1569-1573},
file = {articles/Vogels2011.pdf},
pmid = {22075724}
}
@book{Hastie2009,
address = {New York, NY},
edition = {2nd ed},
series = {Springer series in statistics},
title = {The Elements of Statistical Learning: Data Mining, Inference, and Prediction},
isbn = {978-0-387-84857-0 978-0-387-84858-7},
lccn = {Q325.5 .H39 2009},
shorttitle = {The Elements of Statistical Learning},
publisher = {{Springer}},
author = {Hastie, Trevor and Tibshirani, Robert and Friedman, J. H.},
year = {2009},
keywords = {Bioinformatics,Computational intelligence,Data mining,Forecasting,Inference,Machine learning,Methodology,Statistics},
file = {books/Hastie2009_The-elements-of-statistical-learning-data-mining,-inference,-and-prediction.pdf}
}
@unpublished{Huber-Klawitter2010,
title = {Kommutative {{Algebra}} Und {{Einf\"uhrung}} in Die Algebraische {{Geometrie}} (Mit {{Notizen}})},
author = {{Huber-Klawitter} and Hoffmann, Felix},
year = {2010},
file = {manuscripts/Huber-Klawitter2010_Kommutative-Algebra-und-Einführung-in-die-algebraische-Geometrie-(mit-Notizen).pdf}
}
@article{Platschek2016,
title = {A General Homeostatic Principle Following Lesion Induced Dendritic Remodeling},
volume = {4},
issn = {2051-5960},
doi = {10.1186/s40478-016-0285-8},
abstract = {Neuronal death and subsequent denervation of target areas are hallmarks of many neurological disorders. Denervated neurons lose part of their dendritic tree, and are considered "atrophic", i.e. pathologically altered and damaged. The functional consequences of this phenomenon are poorly understood.},
journal = {Acta Neuropathologica Communications},
author = {Platschek, Steffen and Cuntz, Hermann and Vuksic, Mario and Deller, Thomas and Jedlicka, Peter},
year = {2016},
keywords = {Backpropagating action potential,Compartmental modeling,Computer Simulation,Electrotonic analysis,Granule cell,Homeostatic plasticity,Morphological modeling,Voltage attenuation},
pages = {19},
file = {articles/Platschek2016.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/2J8NJJ2T/s40478-016-0285-8.html}
}
@article{Rochefort2011,
title = {Development of {{Direction Selectivity}} in {{Mouse Cortical Neurons}}},
volume = {71},
issn = {0896-6273},
doi = {10.1016/j.neuron.2011.06.013},
abstract = {Summary
Previous studies of the ferret visual cortex indicate that the development of direction selectivity requires visual experience. Here, we used two-photon calcium imaging to study the development of direction selectivity in layer 2/3 neurons of the mouse visual cortex in~vivo. Surprisingly, just after eye opening nearly all orientation-selective neurons were also direction selective. During later development, the number of neurons responding to drifting gratings increased in parallel with the fraction of neurons that were orientation, but not direction, selective. Our experiments demonstrate that direction selectivity develops normally in dark-reared mice, indicating that the early development of direction selectivity is independent of visual experience. Furthermore, remarkable functional similarities exist between the development of direction selectivity in cortical neurons and the previously reported development of direction selectivity in the mouse retina. Together, these findings provide strong evidence that the development of orientation and direction selectivity in the mouse brain is distinctly different from that in ferrets.},
number = {3},
journal = {Neuron},
author = {Rochefort, Nathalie L. and Narushima, Madoka and Grienberger, Christine and Marandi, Nima and Hill, Daniel N. and Konnerth, Arthur},
month = aug,
year = {2011},
pages = {425-432},
file = {articles/Rochefort2011.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/FTHZG2PV/S0896627311005186.html}
}
@article{Brown2009,
title = {Intracortical Circuits of Pyramidal Neurons Reflect Their Long-Range Axonal Targets},
volume = {457},
copyright = {\textcopyright{} 2009 Nature Publishing Group},
issn = {0028-0836},
doi = {10.1038/nature07658},
abstract = {Cortical columns generate separate streams of information that are distributed to numerous cortical and subcortical brain regions. We asked whether local intracortical circuits reflect these different processing streams by testing whether the intracortical connectivity among pyramidal neurons reflects their long-range axonal targets. We recorded simultaneously from up to four retrogradely labelled pyramidal neurons that projected to the superior colliculus, the contralateral striatum or the contralateral cortex to assess their synaptic connectivity. Here we show that the probability of synaptic connection depends on the functional identities of both the presynaptic and postsynaptic neurons. We first found that the frequency of monosynaptic connections among corticostriatal pyramidal neurons is significantly higher than among corticocortical or corticotectal pyramidal neurons. We then show that the probability of feed-forward connections from corticocortical neurons to corticotectal neurons is approximately three- to fourfold higher than the probability of monosynaptic connections among corticocortical or corticotectal cells. Moreover, we found that the average axodendritic overlap of the presynaptic and postsynaptic pyramidal neurons could not fully explain the differences in connection probability that we observed. The selective synaptic interactions we describe demonstrate that the organization of local networks of pyramidal cells reflects the long-range targets of both the presynaptic and postsynaptic neurons.},
language = {en},
number = {7233},
journal = {Nature},
author = {Brown, Solange P. and Hestrin, Shaul},
month = feb,
year = {2009},
pages = {1133-1136},
file = {articles/Brown2009.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/ZMNVUE9S/nature07658.html}
}
@article{Hodgkin1952,
title = {A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve},
volume = {117},
issn = {1469-7793},
doi = {10.1113/jphysiol.1952.sp004764},
language = {en},
number = {4},
journal = {The Journal of Physiology},
author = {Hodgkin, A. L. and Huxley, A. F.},
month = aug,
year = {1952},
pages = {500-544},
file = {articles/Hodgkin1952.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/4MZ6B758/abstract.html}
}
@book{Meister2015,
address = {Wiesbaden},
edition = {5., \"uberarb. Aufl},
series = {Lehrbuch},
title = {{Numerik linearer Gleichungssysteme: eine Einf\"uhrung in moderne Verfahren ; mit MATLAB-Implementierungen von C. V\"omel}},
isbn = {978-3-658-07200-1 978-3-658-07199-8},
shorttitle = {{Numerik linearer Gleichungssysteme}},
language = {ger},
publisher = {{Springer Spektrum}},
author = {Meister, Andreas},
year = {2015},
keywords = {Lineares Gleichungssystem,Numerisches Verfahren},
file = {articles/Book/Meister20152.pdf;books/Meister2015_Numerik-linearer-Gleichungssysteme-eine-Einführung-in-moderne-Verfahren-\;-mit-MATLAB-Implementierungen-von-C.-Vömel.pdf}
}
@phdthesis{Gjorgjieva2011,
title = {Spontaneous Activity and Plasticity in the Developing Nervous System},
author = {Gjorgjieva, Julijana},
year = {2011},
file = {thesis/Gjorgjieva2011.pdf}
}
@article{Gollo2014,
title = {Mechanisms of {{Zero}}-{{Lag Synchronization}} in {{Cortical Motifs}}},
volume = {10},
issn = {1553-7358},
doi = {10.1371/journal.pcbi.1003548},
abstract = {Author Summary Understanding large-scale neuronal dynamics \textendash{} and how they relate to the cortical anatomy \textendash{} is one of the key areas of neuroscience research. Despite a wealth of recent research, the key principles of this relationship have yet to be established. Here we employ computational modeling to study neuronal dynamics on small subgraphs \textendash{} or motifs \textendash{} across a hierarchy of spatial scales. We establish a novel organizing principle that we term a ``resonance pair'' (two mutually coupled nodes), which promotes stable, zero-lag synchrony amongst motif nodes. The bidirectional coupling between a resonance pair acts to mutually adjust their dynamics onto a common and relatively stable synchronized regime, which then propagates and stabilizes the synchronization of other nodes within the motif. Remarkably, we find that this effect can propagate along chains of coupled nodes and hence holds the potential to promote stable zero-lag synchrony in larger sub-networks of cortical systems. Our findings hence suggest a potential unifying account of the existence of zero-lag synchrony, an important phenomenon that may underlie crucial cognitive processes in the brain. Moreover, such pairs of mutually coupled oscillators are found in a wide variety of physical and biological systems suggesting a new, broadly relevant and unifying principle.},
number = {4},
journal = {PLOS Comput Biol},
author = {Gollo, Leonardo L. and Mirasso, Claudio and Sporns, Olaf and Breakspear, Michael},
month = apr,
year = {2014},
keywords = {Action potentials,Dynamical systems,Membrane potential,Network motifs,Neurons,Single neuron function,Synapses,neural networks},
pages = {e1003548},
file = {articles/Gollo2014.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/F3GUIIHF/article.html}
}
@article{Bi1998,
title = {Synaptic {{Modifications}} in {{Cultured Hippocampal Neurons}}: {{Dependence}} on {{Spike Timing}}, {{Synaptic Strength}}, and {{Postsynaptic Cell Type}}},
volume = {18},
issn = {0270-6474, 1529-2401},
shorttitle = {Synaptic {{Modifications}} in {{Cultured Hippocampal Neurons}}},
abstract = {In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb's rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.},
language = {en},
number = {24},
journal = {The Journal of Neuroscience},
author = {Bi, Guo-qiang and Poo, Mu-ming},
month = dec,
year = {1998},
keywords = {Hebbian,Hebb’s rule,LTD,LTP,cell culture,correlated-activity,hippocampal neurons,plasticity,spike timing,spiking,synaptic modification,target specificity},
pages = {10464-10472},
file = {articles/Bi1998.pdf},
pmid = {9852584}
}
@book{Filo2010,
address = {Hoboken, N.J},
title = {Information Processing by Biochemical Systems: Neural Network-Type Configurations},
isbn = {978-0-470-50094-1},
lccn = {QA76.884 .F55 2010},
shorttitle = {Information Processing by Biochemical Systems},
publisher = {{John Wiley \& Sons}},
author = {Filo, Orna and Lotan, Noah},
year = {2010},
keywords = {Automatic Data Processing,Biochemical Phenomena,Biocomputers,Information technology,Neural Networks (Computer),Neural networks (Computer science)},
file = {books/Filo2010_Information-processing-by-biochemical-systems-neural-network-type-configurations.pdf}
}
@article{Petz1986,
title = {On the Equality in {{Jensen}}'s Inequality for Operator Convex Functions},
volume = {9},
issn = {0378-620X, 1420-8989},
doi = {10.1007/BF01195811},
abstract = {Let A and B be C*-algebras with unit and assume that $\phi{}:$A$\rightarrow$B is a positive unit preserving linear mapping. Choi proved thatf($\Phi$(a))$\leqq\Phi$(f(a))f($\backslash$Phi (a)) $\backslash$leqq $\backslash$Phi (f(a)) if a=a*$\in$A and Sp(a)$\subset$($\alpha$, $\beta$) for every operator convex function f: ($\alpha$, $\beta$) $\rightarrow$ $\mathbb{R}$. We prove that the equality holds if and only if $\phi$ restricted to the subalgebra generated by \{a\} is multiplicative. An example is shown as an application.},
language = {en},
number = {5},
journal = {Integral Equations and Operator Theory},
author = {Petz, D\'enes},
month = sep,
year = {1986},
keywords = {Analysis},
pages = {744-747},
file = {articles/Petz1986.pdf;/home/fh/.mozilla/firefox/d0w5bt9s.default/zotero/storage/VVTVF936/10.html}
}
@article{Hennequin2014,
title = {Optimal {{Control}} of {{Transient Dynamics}} in {{Balanced Networks Supports Generation}} of {{Complex Movements}}},
volume = {82},
issn = {08966273},
doi = {10.1016/j.neuron.2014.04.045},
language = {en},
number = {6},
journal = {Neuron},
author = {Hennequin, Guillaume and Vogels, Tim P. and Gerstner, Wulfram},
month = jun,
year = {2014},
pages = {1394-1406},
file = {articles/Hennequin2014.pdf}
}
@article{Haider2006,
title = {Neocortical {{Network Activity In Vivo Is Generated}} through a {{Dynamic Balance}} of {{Excitation}} and {{Inhibition}}},
volume = {26},
issn = {0270-6474, 1529-2401},
doi = {10.1523/JNEUROSCI.5297-05.2006},
language = {en},
number = {17},
journal = {Journal of Neuroscience},
author = {Haider, B.},
month = apr,
year = {2006},
pages = {4535-4545},
file = {articles/Haider2006.pdf}
}
@book{Cover2006,
address = {Hoboken, N.J},
edition = {2 edition},
title = {Elements of {{Information Theory}} 2nd {{Edition}}},
isbn = {978-0-471-24195-9},
abstract = {The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.},
language = {English},
publisher = {{Wiley-Interscience}},
author = {Cover, Thomas M. and Thomas, Joy A.},
month = jul,
year = {2006},
file = {books/Cover2006_Elements-of-Information-Theory-2nd-Edition.pdf;books/Cover2006_solutions_to_exercises.pdf}
}
@book{Kampen2008,
address = {Amsterdam},
edition = {3. ed., repr},
series = {North-Holland personal library},
title = {Stochastic Processes in Physics and Chemistry},
isbn = {978-0-444-52965-7},
language = {eng},
publisher = {{Elsevier}},
author = {van Kampen, Nicolaas Godfried},
year = {2008},
keywords = {Chemie,Chemistry; Physical and theoretical Statistical methods,Fluktuation,Lehrbuch,Physik,Statistical physics,Stochastic processes,Stochastischer Prozess},
file = {books/Kampen2008_Stochastic-processes-in-physics-and-chemistry.djvu},
note = {OCLC: 315990030}
}
@article{Sachdev2012,
title = {Surround Suppression and Sparse Coding in Visual and Barrel Cortices},
volume = {6},
issn = {1662-5110},
doi = {10.3389/fncir.2012.00043},
abstract = {During natural vision the entire retina is stimulated. Likewise, during natural tactile behaviors, spatially extensive regions of the somatosensory surface are co-activated. The large spatial extent of naturalistic stimulation means that surround suppression, a phenomenon whose neural mechanisms remain a matter of debate, must arise during natural behavior. To identify common neural motifs that might instantiate surround suppression across modalities, we review models of surround suppression and compare the evidence supporting the competing ideas that surround suppression has either cortical or sub-cortical origins in visual and barrel cortex. In the visual system there is general agreement lateral inhibitory mechanisms contribute to surround suppression, but little direct experimental evidence that intracortical inhibition plays a major role. Two intracellular recording studies of V1, one using naturalistic stimuli (Haider et al., ), the other sinusoidal gratings (Ozeki et al., ), sought to identify the causes of reduced activity in V1 with increasing stimulus size, a hallmark of surround suppression. The former attributed this effect to increased inhibition, the latter to largely balanced withdrawal of excitation and inhibition. In rodent primary somatosensory barrel cortex, multi-whisker responses are generally weaker than single whisker responses, suggesting multi-whisker stimulation engages similar surround suppressive mechanisms. The origins of suppression in S1 remain elusive: studies have implicated brainstem lateral/internuclear interactions and both thalamic and cortical inhibition. Although the anatomical organization and instantiation of surround suppression in the visual and somatosensory systems differ, we consider the idea that one common function of surround suppression, in both modalities, is to remove the statistical redundancies associated with natural stimuli by increasing the sparseness or selectivity of sensory responses.},
journal = {Frontiers in Neural Circuits},
author = {Sachdev, Robert N. S. and Krause, Matthew R. and Mazer, James A.},
month = jul,