references.bib

@{biblatex-control,
}

@article{Song2005,
  author =        {Song, Sen and Sj{\"o}str{\"o}m, Per Jesper and
                   Reigl, Markus and Nelson, Sacha and
                   Chklovskii, Dmitri B},
  journal =       {PLoS Biol},
  month =         mar,
  number =        {3},
  pages =         {e68},
  title =         {Highly {{Nonrandom Features}} of {{Synaptic
                   Connectivity}} in {{Local Cortical Circuits}}},
  volume =        {3},
  year =          {2005},
  abstract =      {A dataset of hundreds of recordings in which four
                   neurons were simultaneously monitored reveals
                   clustered connectivity patterns among cortical
                   neurons.},
  doi =           {10.1371/journal.pbio.0030068},
}

@article{Perin2011,
  author =        {Perin, Rodrigo and Berger, Thomas K. and
                   Markram, Henry},
  journal =       {Proceedings of the National Academy of Sciences},
  month =         mar,
  number =        {13},
  pages =         {5419--5424},
  title =         {A Synaptic Organizing Principle for Cortical Neuronal
                   Groups},
  volume =        {108},
  year =          {2011},
  abstract =      {Neuronal circuitry is often considered a clean slate
                   that can be dynamically and arbitrarily molded by
                   experience. However, when we investigated synaptic
                   connectivity in groups of pyramidal neurons in the
                   neocortex, we found that both connectivity and
                   synaptic weights were surprisingly predictable.
                   Synaptic weights follow very closely the number of
                   connections in a group of neurons, saturating after
                   only 20\% of possible connections are formed between
                   neurons in a group. When we examined the network
                   topology of connectivity between neurons, we found
                   that the neurons cluster into small world networks
                   that are not scale-free, with less than 2 degrees of
                   separation. We found a simple clustering rule where
                   connectivity is directly proportional to the number
                   of common neighbors, which accounts for these small
                   world networks and accurately predicts the connection
                   probability between any two neurons. This pyramidal
                   neuron network clusters into multiple groups of a few
                   dozen neurons each. The neurons composing each group
                   are surprisingly distributed, typically more than 100
                   $\mu$m apart, allowing for multiple groups to be
                   interlaced in the same space. In summary, we
                   discovered a synaptic organizing principle that
                   groups neurons in a manner that is common across
                   animals and hence, independent of individual
                   experiences. We speculate that these elementary
                   neuronal groups are prescribed Lego-like building
                   blocks of perception and that acquired memory relies
                   more on combining these elementary assemblies into
                   higher-order constructs.},
  doi =           {10.1073/pnas.1016051108},
  issn =          {0027-8424, 1091-6490},
  language =      {en},
}

@article{Markram1997,
  author =        {Markram, Henry and L{\"u}bke, Joachim and
                   Frotscher, Michael and Roth, Arnd and Sakmann, Bert},
  journal =       {The Journal of Physiology},
  month =         apr,
  number =        {Pt 2},
  pages =         {409--440},
  title =         {Physiology and Anatomy of Synaptic Connections
                   between Thick Tufted Pyramidal Neurones in the
                   Developing Rat Neocortex.},
  volume =        {500},
  year =          {1997},
  abstract =      {1. Dual voltage recordings were made from pairs of
                   adjacent, synaptically connected thick tufted layer 5
                   pyramidal neurones in brain slices of young rat
                   (14-16 days) somatosensory cortex to examine the
                   physiological properties of unitary EPSPs. Pre- and
                   postsynaptic neurones were filled with biocytin and
                   examined in the light and electron microscope to
                   quantify the morphology of axonal and dendritic
                   arbors and the number and location of synaptic
                   contacts on the target neurone. 2. In 138 synaptic
                   connections between pairs of pyramidal neurones 96
                   (70\%) were unidirectional and 42 (30\%) were
                   bidirectional. The probability of finding a synaptic
                   connection in dual recordings was 0.1. Unitary EPSPs
                   evoked by a single presynaptic action potential (AP)
                   had a mean peak amplitude ranging from 0.15 to 5.5 mV
                   in different connections with a mean of 1.3 +/- 1.1
                   mV, a latency of 1.7 +/- 0.9 ms, a 20-80\% rise time
                   of 2.9 +/- 2.3 ms and a decay time constant of 40 +/-
                   18 ms at 32-24 degrees C and -60 +/- 2 mV membrane
                   potential. 3. Peak amplitudes of unitary EPSPs
                   fluctuated randomly from trial to trial. The
                   coefficient of variation (c.v.) of the unitary EPSP
                   amplitudes ranged from 0.13 to 2.8 in different
                   synaptic connections (mean, 0.52; median, 0.41). The
                   percentage of failures of single APs to evoke a
                   unitary EPSP ranged from 0 to 73\% (mean, 14\%;
                   median, 7\%). Both c.v. and percentage of failures
                   decreased with increasing mean EPSP amplitude. 4.
                   Postsynaptic glutamate receptors which mediate
                   unitary EPSPs at -60 mV were predominantly of the
                       L-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate
  (AMPA) receptor type. Receptors of the N-methyl-D-aspartate (NMDA) type
  contributed only a small fraction ($<$ 20\%) to the voltage-time integral of
  the unitary EPSP at -60 mV, but their contribution increased at more positive
  membrane potentials. 5. Branching patterns of dendrites and axon collaterals
  of forty-five synaptically connected neurones, when examined in the light
  microscope, indicated that the axonal and dendritic anatomy of both
  projecting and target neurones and of uni- and bidirectionally connected
  neurones was uniform. 6. The number of potential synaptic contacts formed by
  a presynaptic neurone on a target neurone varied between four and eight
  (mean, 5.5 +/- 1.1 contacts; n = 19 connections). Synaptic contacts were
  preferentially located on basal dendrites (63\%, 82 +/- 35 microns from the
  soma, n = 67) and apical oblique dendrites (27\%, 145 +/- 59 microns, n =
  29), and 35\% of all contacts were located on tertiary basal dendritic
  branches. The mean geometric distances (from the soma) of the contacts of a
  connection varied between 80 and 585 microns (mean, 147 microns; median, 105
  microns). The correlation between EPSP amplitude and the number of
  morphologically determined synaptic contacts or the mean geometric distances
  from the soma was only weak (correlation coefficients were 0.2 and 0.26,
  respectively). 7. Compartmental models constructed from camera lucida
  drawings of eight target neurones showed that synaptic contacts were located
  at mean electrotonic distances between 0.07 and 0.33 from the soma (mean,
  0.13). Simulations of unitary EPSPs, assuming quantal conductance changes
  with fast rise time and short duration, indicated that amplitudes of quantal
  EPSPs at the soma were attenuated, on average, to $<$ 10\% of dendritic EPSPs
  and varied in amplitude up to 10-fold depending on the dendritic location of
  synaptic contacts. The inferred quantal peak conductance increase varied
  between 1.5 and 5.5 nS (mean, 3 nS). 8. The combined physiological and
  morphological measurements in conjunction with EPSP simulations indicated
  that the 20-fold range in efficacy of the synaptic connections between thick
  tufted pyramidal neurones, which have their synaptic contacts preferentially
  located on basal and apical oblique dendrites, was due to differences in
  transmitter release probability of the projecting neurones and, to a lesser
  extent, to differenc},
  issn =          {0022-3751},
}

@article{Lefort2009,
  author =        {Lefort, Sandrine and Tomm, Christian and
                   Floyd Sarria, J. -C. and Petersen, Carl C. H.},
  journal =       {Neuron},
  month =         jan,
  number =        {2},
  pages =         {301--316},
  title =         {The {{Excitatory Neuronal Network}} of the {{C2
                   Barrel Column}} in {{Mouse Primary Somatosensory
                   Cortex}}},
  volume =        {61},
  year =          {2009},
  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.},
  doi =           {10.1016/j.neuron.2008.12.020},
  issn =          {0896-6273},
}

@article{Bourjaily2011,
  author =        {Bourjaily, Mark A. and Miller, Paul},
  journal =       {Frontiers in Computational Neuroscience},
  pages =         {37},
  title =         {Excitatory, Inhibitory, and Structural Plasticity
                   Produce Correlated Connectivity in Random Networks
                   Trained to Solve Paired-Stimulus Tasks},
  volume =        {5},
  year =          {2011},
  abstract =      {The pattern of connections among cortical excitatory
                   cells with overlapping arbors is non-random. In
                   particular, correlations among connections produce
                   clustering \textendash{} cells in cliques connect to
                   each other with high probability, but with lower
                   probability to cells in other spatially intertwined
                   cliques. In this study, we model initially randomly
                   connected sparse recurrent networks of spiking
                   neurons with random, overlapping inputs, to
                   investigate what functional and structural synaptic
                   plasticity mechanisms sculpt network connections into
                   the patterns measured in vitro. Our Hebbian
                   implementation of structural plasticity causes a
                   removal of connections between uncorrelated
                   excitatory cells, followed by their random
                   replacement. To model a biconditional discrimination
                   task, we stimulate the network via pairs (A + B, C +
                   D, A + D, and C + B) of four inputs (A, B, C, and D).
                   We find networks that produce neurons most responsive
                   to specific paired inputs \textendash{} a building
                   block of computation and essential role for cortex
                   \textendash{} contain the excessive clustering of
                   excitatory synaptic connections observed in cortical
                   slices. The same networks produce the best
                   performance in a behavioral readout of the networks'
                   ability to complete the task. A plasticity mechanism
                   operating on inhibitory connections, long-term
                   potentiation of inhibition, when combined with
                   structural plasticity, indirectly enhances clustering
                   of excitatory cells via excitatory connections. A
                   rate-dependent (triplet) form of
                   spike-timing-dependent plasticity (STDP) between
                   excitatory cells is less effective and basic STDP is
                   detrimental. Clustering also arises in networks
                   stimulated with single stimuli and in networks
                   undergoing raised levels of spontaneous activity when
                   structural plasticity is combined with functional
                   plasticity. In conclusion, spatially intertwined
                   clusters or cliques of connected excitatory cells can
                   arise via a Hebbian form of structural plasticity
                   operating in initially randomly connected networks.},
  doi =           {10.3389/fncom.2011.00037},
}

@article{Gilbert1959,
  author =        {Gilbert, E. N.},
  journal =       {The Annals of Mathematical Statistics},
  month =         dec,
  note =          {Mathematical Reviews number (MathSciNet) MR108839,
                   Zentralblatt MATH identifier0168.40801},
  number =        {4},
  pages =         {1141--1144},
  title =         {Random {{Graphs}}},
  volume =        {30},
  year =          {1959},
  abstract =      {Project Euclid - mathematics and statistics online},
  doi =           {10.1214/aoms/1177706098},
  issn =          {0003-4851, 2168-8990},
  language =      {EN},
}

@article{Erdos1959,
  author =        {Erd{\H o}s, P. and R{\'e}nyi, A.},
  journal =       {Publicationes Mathematicae (Debrecen)},
  pages =         {290--297},
  title =         {On Random Graphs, {{I}}},
  volume =        {6},
  year =          {1959},
}

@article{Jensen1906,
  author =        {Jensen, J. L. W. V.},
  journal =       {Acta Mathematica},
  month =         dec,
  number =        {1},
  pages =         {175--193},
  title =         {{Sur les fonctions convexes et les in{\'e}galit{\'e}s
                   entre les valeurs moyennes}},
  volume =        {30},
  year =          {1906},
  doi =           {10.1007/BF02418571},
  issn =          {0001-5962, 1871-2509},
  language =      {fr},
}

@book{Cover2006,
  address =       {Hoboken, N.J},
  author =        {Cover, Thomas M. and Thomas, Joy A.},
  edition =       {2 edition},
  month =         jul,
  publisher =     {{Wiley-Interscience}},
  title =         {Elements of {{Information Theory}} 2nd {{Edition}}},
  year =          {2006},
  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.},
  isbn =          {978-0-471-24195-9},
  language =      {English},
}

@article{Thomson2002,
  author =        {Thomson, Alex M. and West, David C. and Wang, Yun and
                   Bannister, A. Peter},
  journal =       {Cerebral Cortex},
  month =         jan,
  number =        {9},
  pages =         {936--953},
  title =         {Synaptic {{Connections}} and {{Small Circuits
                   Involving Excitatory}} and {{Inhibitory Neurons}} in
                   {{Layers}} 2\textendash{}5 of {{Adult Rat}} and {{Cat
                   Neocortex}}: {{Triple Intracellular Recordings}} and
                   {{Biocytin Labelling In Vitro}}},
  volume =        {12},
  year =          {2002},
  abstract =      {Dual and triple intracellular recordings with
                   biocytin labelling in slices of adult neocortex
                   explored small circuits of synaptically connected
                   neurons. 679 paired recordings in rat and 319 in cat
                   yielded 135 and 42 excitatory postsynaptic potentials
                   (EPSPs) and 37 and 26 inhibitory postsynaptic
                   potentials (IPSPs), respectively. Patterns of
                   connectivity and synaptic properties were similar in
                   the two species, although differences of scale and in
                   the range of morphologies were observed. Excitatory
                   `forward' projections from layer 4 to 3, like those
                   from layer 3 to 5, targeted pyramidal cells and a
                   small proportion of interneurons, while excitatory
                   `back' projections from layer 3 to 4 selected
                   interneurons, including parvalbumin immuno-positive
                   basket cells. Layer 4 interneurons that inhibited
                   layer 3 pyramidal cells included both basket cells
                   and dendrite-targeting cells. Large interneurons,
                   resembling cells previously described as large basket
                   cells, in layers 4 and 3 (cat), with long myelinated
                   horizontal axon collaterals received frequent
                   excitatory inputs from both layers. A very high rate
                   of connectivity was observed between pairs of
                   interneurons, often with quite different
                   morphologies, and the resultant IPSPs, like the EPSPs
                   recorded in interneurons, were brief compared with
                   those recorded in pyramidal and spiny stellate
                   cells.},
  doi =           {10.1093/cercor/12.9.936},
  issn =          {1047-3211, 1460-2199},
  language =      {en},
}

@article{Lee2016a,
  author =        {Lee, Wei-Chung Allen and Bonin, Vincent and
                   Reed, Michael and Graham, Brett J. and Hood, Greg and
                   Glattfelder, Katie and Reid, R. Clay},
  journal =       {Nature},
  month =         mar,
  title =         {Anatomy and Function of an Excitatory Network in the
                   Visual Cortex},
  year =          {2016},
  doi =           {10.1038/nature17192},
  issn =          {0028-0836, 1476-4687},
}

@book{Hogg1978,
  address =       {New York},
  author =        {Hogg, Robert V. and Craig, Allen T.},
  edition =       {4th ed},
  publisher =     {{Macmillan}},
  title =         {Introduction to Mathematical Statistics},
  year =          {1978},
  isbn =          {978-0-02-355710-1},
}

@article{Ko2011,
  author =        {Ko, Ho and Hofer, Sonja B. and Pichler, Bruno and
                   Buchanan, Katherine A. and
                   Sj{\"o}str{\"o}m, P. Jesper and
                   Mrsic-Flogel, Thomas D.},
  journal =       {Nature},
  month =         may,
  number =        {7345},
  pages =         {87--91},
  title =         {Functional Specificity of Local Synaptic Connections
                   in Neocortical Networks},
  volume =        {473},
  year =          {2011},
  abstract =      {Neuronal connectivity is fundamental to information
                   processing in the brain. Therefore, understanding the
                   mechanisms of sensory processing requires uncovering
                   how connection patterns between neurons relate to
                   their function. On a coarse scale, long-range
                   projections can preferentially link cortical regions
                   with similar responses to sensory stimuli. But on the
                   local scale, where dendrites and axons overlap
                   substantially, the functional specificity of
                   connections remains unknown. Here we determine
                   synaptic connectivity between nearby layer 2/3
                   pyramidal neurons in vitro, the response properties
                   of which were first characterized in mouse visual
                   cortex in vivo. We found that connection probability
                   was related to the similarity of visually driven
                   neuronal activity. Neurons with the same preference
                   for oriented stimuli connected at twice the rate of
                   neurons with orthogonal orientation preferences.
                   Neurons responding similarly to naturalistic stimuli
                   formed connections at much higher rates than those
                   with uncorrelated responses. Bidirectional synaptic
                   connections were found more frequently between
                   neuronal pairs with strongly correlated visual
                   responses. Our results reveal the degree of
                   functional specificity of local synaptic connections
                   in the visual cortex, and point to the existence of
                   fine-scale subnetworks dedicated to processing
                   related sensory information.},
  doi =           {10.1038/nature09880},
  issn =          {0028-0836},
  language =      {en},
}

@article{Stepanyants2004,
  author =        {Stepanyants, Armen and Tam{\'a}s, G{\'a}bor and
                   Chklovskii, Dmitri B.},
  journal =       {Neuron},
  month =         jul,
  number =        {2},
  pages =         {251--259},
  title =         {Class-{{Specific Features}} of {{Neuronal Wiring}}},
  volume =        {43},
  year =          {2004},
  abstract =      {Brain function relies on specificity of synaptic
                   connectivity patterns among different classes of
                   neurons. Yet, the substrates of specificity in
                   complex neuropil remain largely unknown. We search
                   for imprints of specificity in the layout of axonal
                   and dendritic arbors from the rat neocortex. An
                   analysis of 3D reconstructions of pairs consisting of
                   pyramidal cells (PCs) and GABAergic interneurons
                   (GIs) revealed that the layout of GI axons is
                   specific. This specificity is manifested in a
                   relatively high tortuosity, small branch length of
                   these axons, and correlations of their trajectories
                   with the positions of postsynaptic neuron dendrites.
                   Axons of PCs show no such specificity, usually taking
                   a relatively straight course through neuropil.
                   However, wiring patterns among PCs hold a large
                   potential for circuit remodeling and specificity
                   through growth and retraction of dendritic spines.
                   Our results define distinct class-specific rules in
                   establishing synaptic connectivity, which could be
                   crucial in formulating a canonical cortical circuit.},
  doi =           {10.1016/j.neuron.2004.06.013},
  issn =          {0896-6273},
  language =      {English},
}

@article{Kalisman2005,
  author =        {Kalisman, Nir and Silberberg, Gilad and
                   Markram, Henry},
  journal =       {Proceedings of the National Academy of Sciences of
                   the United States of America},
  month =         jan,
  number =        {3},
  pages =         {880--885},
  title =         {The Neocortical Microcircuit as a Tabula Rasa},
  volume =        {102},
  year =          {2005},
  abstract =      {The neocortex has a high capacity for plasticity. To
                   understand the full scope of this capacity, it is
                   essential to know how neurons choose particular
                   partners to form synaptic connections. By using
                   multineuron whole-cell recordings and confocal
                   microscopy we found that axons of layer V neocortical
                   pyramidal neurons do not preferentially project
                   toward the dendrites of particular neighboring
                   pyramidal neurons; instead, axons promiscuously touch
                   all neighboring dendrites without any bias.
                   Functional synaptic coupling of a small fraction of
                   these neurons is, however, correlated with the
                   existence of synaptic boutons at existing touch
                   sites. These data provide the first direct
                   experimental evidence for a tabula rasa-like
                   structural matrix between neocortical pyramidal
                   neurons and suggests that pre- and postsynaptic
                   interactions shape the conversion between touches and
                   synapses to form specific functional microcircuits.
                   These data also indicate that the local neocortical
                   microcircuit has the potential to be differently
                   rewired without the need for remodeling axonal or
                   dendritic arbors.},
  doi =           {10.1073/pnas.0407088102},
  issn =          {0027-8424, 1091-6490},
  language =      {en},
}

@article{Clopath2010,
  author =        {Clopath, Claudia and B{\"u}sing, Lars and
                   Vasilaki, Eleni and Gerstner, Wulfram},
  journal =       {Nature Neuroscience},
  month =         mar,
  number =        {3},
  pages =         {344--352},
  title =         {Connectivity Reflects Coding: A Model of
                   Voltage-Based {{STDP}} with Homeostasis},
  volume =        {13},
  year =          {2010},
  abstract =      {Electrophysiological connectivity patterns in cortex
                   often have a few strong connections, which are
                   sometimes bidirectional, among a lot of weak
                   connections. To explain these connectivity patterns,
                   we created a model of spike
                   timing\textendash{}dependent plasticity (STDP) in
                   which synaptic changes depend on presynaptic spike
                   arrival and the postsynaptic membrane potential,
                   filtered with two different time constants. Our model
                   describes several nonlinear effects that are observed
                   in STDP experiments, as well as the voltage
                   dependence of plasticity. We found that, in a
                   simulated recurrent network of spiking neurons, our
                   plasticity rule led not only to development of
                   localized receptive fields but also to connectivity
                   patterns that reflect the neural code. For temporal
                   coding procedures with spatio-temporal input
                   correlations, strong connections were predominantly
                   unidirectional, whereas they were bidirectional under
                   rate-coded input with spatial correlations only.
                   Thus, variable connectivity patterns in the brain
                   could reflect different coding principles across
                   brain areas; moreover, our simulations suggested that
                   plasticity is fast.},
  doi =           {10.1038/nn.2479},
  issn =          {1097-6256},
  language =      {en},
}

@article{Miner2016,
  author =        {Miner, Daniel and Triesch, Jochen},
  editor =        {Sporns, Olaf},
  journal =       {PLOS Computational Biology},
  month =         feb,
  number =        {2},
  pages =         {e1004759},
  title =         {Plasticity-{{Driven Self}}-{{Organization}} under
                   {{Topological Constraints Accounts}} for
                   {{Non}}-Random {{Features}} of {{Cortical Synaptic
                   Wiring}}},
  volume =        {12},
  year =          {2016},
  doi =           {10.1371/journal.pcbi.1004759},
  issn =          {1553-7358},
  language =      {en},
}

@article{Hopfield1982,
  author =        {Hopfield, J J},
  journal =       {Proceedings of the National Academy of Sciences of
                   the United States of America},
  month =         apr,
  number =        {8},
  pages =         {2554--2558},
  title =         {Neural Networks and Physical Systems with Emergent
                   Collective Computational Abilities.},
  volume =        {79},
  year =          {1982},
  abstract =      {Computational properties of use of biological
                   organisms or to the construction of computers can
                   emerge as collective properties of systems having a
                   large number of simple equivalent components (or
                   neurons). The physical meaning of content-addressable
                   memory is described by an appropriate phase space
                   flow of the state of a system. A model of such a
                   system is given, based on aspects of neurobiology but
                   readily adapted to integrated circuits. The
                   collective properties of this model produce a
                   content-addressable memory which correctly yields an
                   entire memory from any subpart of sufficient size.
                   The algorithm for the time evolution of the state of
                   the system is based on asynchronous parallel
                   processing. Additional emergent collective properties
                   include some capacity for generalization, familiarity
                   recognition, categorization, error correction, and
                   time sequence retention. The collective properties
                   are only weakly sensitive to details of the modeling
                   or the failure of individual devices.},
  issn =          {0027-8424},
}

@book{Abeles1982,
  address =       {Berlin, Heidelberg},
  author =        {Abeles, Moshe},
  note =          {OCLC: 858930943},
  publisher =     {{Springer Berlin Heidelberg}},
  title =         {Local {{Cortical Circuits}} an {{Electrophysiological
                   Study}}},
  year =          {1982},
  isbn =          {978-3-642-81708-3 978-3-642-81710-6},
  language =      {English},
}

@article{Diesmann1999,
  author =        {Diesmann, Markus and Gewaltig, Marc-Oliver and
                   Aertsen, Ad},
  journal =       {Nature},
  month =         dec,
  number =        {6761},
  pages =         {529--533},
  title =         {Stable Propagation of Synchronous Spiking in Cortical
                   Neural Networks},
  volume =        {402},
  year =          {1999},
  abstract =      {The classical view of neural coding has emphasized
                   the importance of information carried by the rate at
                   which neurons discharge action potentials. More
                   recent proposals that information may be carried by
                   precise spike timing have been challenged by the
                   assumption that these neurons operate in a noisy
                   fashion\textemdash{}presumably reflecting
                   fluctuations in synaptic input\textemdash{}and, thus,
                   incapable of transmitting signals with millisecond
                   fidelity. Here we show that precisely synchronized
                   action potentials can propagate within a model of
                   cortical network activity that recapitulates many of
                   the features of biological systems. An attractor,
                   yielding a stable spiking precision in the
                   (sub)millisecond range, governs the dynamics of
                   synchronization. Our results indicate that a
                   combinatorial neural code, based on rapid
                   associations of groups of neurons co-ordinating their
                   activity at the single spike level, is possible
                   within a cortical-like network.},
  doi =           {10.1038/990101},
  issn =          {0028-0836},
  language =      {en},
}