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Cortex: Statistics and Geometry of Neuronal Connectivity
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“This is because the high levels of recurrent connectivity in the source area (Braitenberg and Schüz 1998; Markov et al. 2011) can lead to labeled axon terminals in the vicinity of the retrogradely labeled cells that can be mistaken for long-distance anterograde labeling from the injected target area.”
From A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex (2014) · cites this paper as Braitenberg and Schüz 1998
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Definitions of state variables and state space for brain-computer interface
Freeman, W. (2006) · Cognitive Neurodynamics · doi:10.1007/s11571-006-9001-x · Cited by 71
“About 85% of cortical neurons are pyramidal neurons (Sholl, 1956; Braitenberg and Schüz, 1998).”
BackgroundInfluentialMerchán-Pérez, Á., Rodríguez, J., González, S., Robles, V., DeFelipe, J., Larrañaga, P., & Bielza, C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht018 · Cited by 68
“This is related to the so called Peter ’ s rule (Braitenberg and Schüz 1998), which represents a generalization to all types of cortical synapses based on the conclusion of Peters and Feldman (1976) regarding the synaptic connections of geniculo-cortical afferents and cortical neurons in the rat…”
BackgroundFast activity as a surrogate marker of epileptic network function?
Rampp, S., & Stefan, H. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.02.023 · Cited by 70
Auditory verb perception recruits motor systems in the developing brain: an fMRI investigation
James, K. H., & Maouene, J. (2009) · Developmental Science · doi:10.1111/j.1467-7687.2009.00919.x · Cited by 77
“All stimuli elicited the same neural response in these regions.”
BackgroundCell-type identity: a key to unlocking the function of neocortical circuits
Brown, S. P., & Hestrin, S. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.07.011 · Cited by 70
“Cell-type specificity and Peters’ rule One model of the organization of pyramidal cell circuits is often described as a generalization of Peters’ rule [33,34].”
Calì, C., Wawrzyniak, M., Becker, C., Maco, B., Cantoni, M., Jorstad, A., Nigro, B., Grillo, F. W., Paola, V. D., Fua, P., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198131 · Cited by 68
Symmetry, Multistability, and Long-Range Interactions in Brain Development
Wolf, F. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.208701 · Cited by 68
Toma, K., & Hanashima, C. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00274 · Cited by 74
Alle, H., Kubota, H., & Geiger, J. R. P. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0972-11.2011 · Cited by 68
“Because the surface of single neocortical en passant boutons is only 0.5–1 m 2 (Braitenberg and Schüz, 1998) and GABAergic interneurons contain high amounts of Kv3 mRNA and protein (Rudy and McBain, 2001), respective small signals in glutamatergic neurons might have stayed undetected in anatomical…”
BackgroundInfluentialSafaai, H., Neves, R. M., Eschenko, O., Logothetis, N. K., & Panzeri, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516539112 · Cited by 68
“Because most cortico-cortical synapses connect proximal neurons (27), the state variables will often be shared by the neuron sending a message and the neuron receiving it; thus, state-dependent decoding may work well for many cortical computations.”
BackgroundNeurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex
Henningsen‐Schomers, M. R., Garagnani, M., & Friedemann Pulvermüller, A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2693-16.2017 · Cited by 70
“1B, green-colored arrows (e.g., A1 to AB, AB to PB); Pandya and Yeterian, 1985; Braitenberg and Pulvermüller, 1992; Pandya, 1994; Young et al., 1994, 1995; Kaas and Hackett, 2000; Rauschecker and Tian, 2000].”
BackgroundCell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortexPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525290 · Cited by 70
Vanni, S., Hokkanen, H., Werner, F., & Angelucci, A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz322 · Cited by 66
“Moreover, one needs to assume that synapses form between cell types with equal probability (Peters’ rule, Braitenberg and Schuz 1998).”
BackgroundLocal cortical circuit model inferred from power-law distributed neuronal avalanches
Teramae, J.-N., & Fukai, T. (2007) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-0014-6 · Cited by 63
“Cortical microcircuit is the fundamental functional unit of information processing in the cortex, and much effort has been made to clarify the neuronal wiring of cortical neurons (Braitenberg and Schuz, 1998; Holmgren et al., 2003; Gupta et al., 2000; Stepanyants et al., 2004; Kalisman et al., 2005; Foldy et al., 2005…”
BackgroundSynEM, automated synapse detection for connectomics
Staffler, B., Berning, M., Boergens, K. M., Gour, A., Smagt, P. V. D., & Helmstaedter, M. (2017) · eLife · doi:10.7554/elife.26414 · Cited by 62
“(b) Number of synapses and neurons, total axonal, dendritic and spine path length for the example datasets in (a) (White and Peters, 1993; Braitenberg and Schüz, 1998; Merchán-Pérez et al., 2014).”
BackgroundMethodTomasello, R., Garagnani, M., Wennekers, T., & Friedemann Pulvermüller, A. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00088 · Cited by 67
Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior
Shmiel, T., Drori, R., Shmiel, O., Ben‐Shaul, Y., Nádasdy, Z., Shemesh, M., Teicher, M., & Abeles, M. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0509346102 · Cited by 64
“[1] Bar-Ilan University, Ramat-Gan 52900, Israel [2] Hebrew University, Jerusalem 91120, Israel [3] California Institute of Technology, Pasadena, 91125, USA”
BackgroundMorphological homeostasis in cortical dendrites
Samsonovich, A. V., & Ascoli, G. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510057103 · Cited by 63
The Neurocognition of Translation and Interpreting
García, A. M. (2019) · Benjamins translation library · doi:10.1075/btl.147 · Cited by 68
Computer Models and Analysis Tools for Neural Microcircuits
Natschläger, T., Markram, H., & Maass, W. (2003) · Springer eBooks · doi:10.1007/978-1-4615-1079-6_9 · Cited by 63
Müller, E. J., Munn, B., & Shine, J. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19716-7 · Cited by 57
Neuron network activity scales exponentially with synapse density
Brewer, G. J., Boehler, M. D., Pearson, R. A., DeMaris, A. A., Ide, A. N., & Wheeler, B. C. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/1/014001 · Cited by 65
Kumarasinghe, K., Kasabov, N. K., & Taylor, D. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.029 · Cited by 66
Müller, E. J., Munn, B., Redinbaugh, M. J., Lizier, J. T., Breakspear, M. J., Saalmann, Y. B., & Shine, J. M. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112844 · Cited by 58
Self-organization of multiple spatial and context memories in the hippocampus
Stella, F., Cerasti, E., Si, B., Ježek, K., & Treves, A. (2011) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.12.002 · Cited by 70
A data-driven framework for neural field modeling
Freestone, D. R., Aram, P., Dewar, M., Scerri, K., Grayden, D. B., & Kadirkamanathan, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.027 · Cited by 59
“In certain regions of the cortex, each column is thought to be connected locally via symmetric short range local excitation with surround inhibition (Braitenberg and Schüz, 1998).”
BackgroundA neuronal model of the language cortex
Garagnani, M., Wennekers, T., & Friedemann Pulvermüller, A. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.076 · Cited by 62
“This led to topographic and patchy connectivity, a pattern typically found in the mammalian cortex [5] [6] [9].”
BackgroundGaragnani, M., Wennekers, T., & Friedemann Pulvermüller, A. (2009) · Cognitive Computation · doi:10.1007/s12559-009-9011-1 · Cited by 63
Astrocyte as Spatiotemporal Integrating Detector of Neuronal Activity
Gordleeva, S. Y., Ermolaeva, A. V., Kastalskiy, I. A., & Kazantsev, V. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00294 · Cited by 58
“We use random coupling topology with connection probability for each pair of neurons equal to 20% (Braitenberg and Schüz, 1998).”
MethodHow environment and genes shape the adolescent brain
Paus, T. (2013) · Hormones and Behavior · doi:10.1016/j.yhbeh.2013.04.004 · Cited by 59
Dissipation of ‘dark energy’ by cortex in knowledge retrieval
Capolupo, A., Freeman, W., & Vitiello, G. (2013) · Physics of Life Reviews · doi:10.1016/j.plrev.2013.01.001 · Cited by 59
Fingelkurts, A. A., Fingelkurts, A. A., Kivisaari, R., Pekkonen, E., Ilmoniemi, R. J., & Kähkönen, S. (2004) · Human Brain Mapping · doi:10.1002/hbm.20014 · Cited by 60
“This is probably motivated by the fact that in many areas of the cortex, there are about four times more excitatory than inhibitory neurons [Braitenberg and Schuz, 1998].”
BackgroundCrossNets: possible neuromorphic networks based on nanoscale components
Türel, Ö., & Likharev, K. K. (2003) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.223 · Cited by 59
Thermodynamic constraints on neural dimensions, firing rates, brain temperature and size
Karbowski, J. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0153-7 · Cited by 58
“Since the average values of cortical axon and dendrite diameters are roughly invariant with respect to brain size (i.e., they should be more or less the same for mouse and human; Braitenberg and Schüz 1998), we can expect that the effective fiber diameter d should not change with brain size either.”
BackgroundMethodInfluentialApical dendrite activity in cognition and consciousness
LaBerge, D. (2005) · Consciousness and Cognition · doi:10.1016/j.concog.2005.09.007 · Cited by 55
How Visual Cortical Organization Is Altered by Ophthalmologic and Neurologic Disorders
Dumoulin, S. O., & Knapen, T. H. J. (2018) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091517-033948 · Cited by 56
Transitions between multistable states as a model of epileptic seizure dynamics
Takeshita, D., Sato, Y. D., & Bahar, S. (2007) · Physical Review E · doi:10.1103/physreve.75.051925 · Cited by 56
Learning from connectomics on the fly
Schlegel, P., Costa, M., & Jefferis, G. S. X. E. (2017) · Current Opinion in Insect Science · doi:10.1016/j.cois.2017.09.011 · Cited by 49
“Assuming a cable density of about 4.6km/mm 3 (mouse cortex, [53,54]) and a manual reconstruction speed of about 0.1mm/h, annotation of a 1 mm 3 volume would take 22,115 person-years to complete.”
MethodA complete electron microscopy volume of the brain of adult Drosophila melanogaster
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Lee, C., Lee, B. H., Jung, H., Lee, C., Sung, Y., Kim, H., Kim, J., Shim, J. Y., Kim, J., Choi, D. I., et al. (2023) · Current Biology · doi:10.1016/j.cub.2022.12.038 · Cited by 56
Relations Between the Geometry of Cortical Gyrification and White-Matter Network Architecture
Henderson, J. A., & Robinson, P. A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0183 · Cited by 53
“Briefly, the construction of the network model consists of parcellating the cortical surface into nodes of similarly sized areas.”
Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands
van Albada, S. J., & Robinson, P. A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00056 · Cited by 54
“Approximating connections in the cortex as random leads to Gii=Gei, Gie=Gee, and Gis=Ges (Braitenberg and Schüz, 1998; Robinson et al., 2001b).”
MethodAnatomical connectivity and the resting state activity of large cortical networks
Pinotsis, D. A., Hansen, E. C. A., Friston, K. J., & Jirsa, V. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.016 · Cited by 56
“Also, the work of anatomists such as (Braitenberg and Schüz, 1998) has revealed that the connection kernel in the cortex exhibits strong homogeneity traits.”
BackgroundReal-time automated EEG tracking of brain states using neural field theory
Abeysuriya, R., & Robinson, P. A. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.09.026 · Cited by 51
NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics
Sanz‐Leon, P., Robinson, P. A., Knock, S. A., Drysdale, P. M., Abeysuriya, R., Fung, F., RENNIE, C. J., & Zhao, X. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006387 · Cited by 57
Optimal Sizes of Dendritic and Axonal Arbors in a Topographic Projection
Chklovskii, D. B. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.83.4.2113 · Cited by 53
Wallace, D. J., & Kerr, J. N. D. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.05.003 · Cited by 53
Homeostatic structural plasticity increases the efficiency of small-world networks
Butz, M., Steenbuck, I. D., & van Ooyen, A. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00007 · Cited by 53
“Alternatively, the formation of local connectivity may basically be random (Braitenberg and Schüz, 1998), just depending on the accidental overlap of axons and dendrites (Binzegger et al., 2004; van Pelt and van Ooyen, 2013; McAssey et al., 2014; van Ooyen et al., 2014).”
BackgroundUnderstanding fiber mixture by simulation in 3D Polarized Light Imaging
Dohmen, M., Menzel, M., Wiese, H., Reckfort, J., Hanke, F. D., Pietrzyk, U., Zilles, K., Amunts, K., & Axer, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.020 · Cited by 52
“Although several studies about fiber densities in the human brain commonly agree on a volume density of fibers of 30–40% (Braitenberg and Schüz, 1998; Marner et al., 2003; Taban et al., 2005), the myelin density must be assumed to vary significantly, e.”
Reading spike timing without a clock: intrinsic decoding of spike trains
Panzeri, S., Ince, R. A. A., Diamond, M. E., & Kayser, C. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0467 · Cited by 53
Nonlinear manifolds underlie neural population activity during behaviourPreprint
Fortunato, C., Bennasar-Vázquez, J., Park, J., Chang, J. C., Miller, L. E., Dudman, J. T., Perich, M. G., & Gallego, J. Á. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.18.549575 · Cited by 53
“Moreover, each neuron makes up to thousands of connections with other neurons, creating intricate connectivity patterns 44–46 .”
Neural Field Models: A mathematical overview and unifying framework
Cook, B. J., Peterson, A. D. H., Woldman, W., & Terry, J. R. (2022) · Mathematical Neuroscience and Applications · doi:10.46298/mna.7284 · Cited by 48
“It should be noted that 97% of connections in cortical tissue are to other regions of cortex [Braitenberg and Schüz, 2013].”
BackgroundAgarwal, N., & Renshaw, P. F. (2011) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2587 · Cited by 51
“Almost 85% of all synapses in the brain are glutamatergic, making glutamate a central molecule in the brain.(1) Besides its”
BackgroundSecondary instabilities modulate cortical complexity in the mammalian brain
Budday, S., Steinmann, P., & Ellen Kuhl, A. (2015) · The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · doi:10.1080/14786435.2015.1024184 · Cited by 53
Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons
Chapeton, J. I., Fares, T., LaSota, D., & Stepanyants, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211467109 · Cited by 51
Detection of the optimal neuron traces in confocal microscopy images
Vasilkoski, Z., & Stepanyants, A. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.11.008 · Cited by 53
“branches is roughly three times smaller (Braitenberg and Schüz, 1998).”
BackgroundInfluentialConnectivity concepts in neuronal network modeling
Senk, J., Kriener, B., Djurfeldt, M., Voges, N., Jiang, H.-J., Schüttler, L., Gramelsberger, G., Diesmann, M., Pleßer, H. E., & van Albada, S. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010086 · Cited by 52
“Elsewhere, for instance in the cerebral cortex, the neuron-level connectivity appears more random [10,11].”
BackgroundHeld, R. G., Liang, J., & Brünger, A. T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403136121 · Cited by 56
“Glutamatergic synapses constitute the majority of syn apses in the brain (1).”
BackgroundNonlocal mechanism for cluster synchronization in neural circuits
Kanter, I., Kopelowitz, E., Vardi, R., Zigzag, M., Kinzel, W., Abeles, M., & Cohen, D. (2011) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/93/66001 · Cited by 63
“In terms of biological properties it was assumed that distant cortico-cortical connections are (almost) exclusively excitatory whereas local connections are both excitatory and inhibitory [14, 15].”
BackgroundInfluentialAnalogues of simple and complex cells in rhesus monkey auditory cortex
Tian, B., Kuśmierek, P., & Rauschecker, J. P. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221062110 · Cited by 48
“If confirmed, both findings would provide another link toward the argument that cortical columns are made up of the same circuitry across different regions and carry out similar computational operations, but apply them to different input signals (8, 35, 43).”
BackgroundGaragnani, M., & Friedemann Pulvermüller, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.031 · Cited by 55
“Local reciprocal connections between excitatory and inhibitory layers of each area realise lateral inhibition (Braitenberg & Schüz, 1998), as follows: each inhibitory cell I receives excitatory input from all excitatory cells situated within an overlying 5x5 neighbourhood and projects back to the…”
Abeles, M. (2004) · Science · doi:10.1126/science.1097725 · Cited by 50
Cortical Models Onto CMOL and CMOS— Architectures and Performance/Price
Gao, C., & Hammerstrom, D. W. (2007) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2007.907830 · Cited by 50
“Braitenberg and Schüz [26] have shown that there are geographically close groups of neurons that are tightly connected with each other and then are sparsely, and more randomly connected to other groups.”
BackgroundSustained attention and apical dendrite activity in recurrent circuits.
LaBerge, D. (2005) · Brain Research Reviews · doi:10.1016/j.brainresrev.2005.04.004 · Cited by 49
Interictal discharges in the human brain are travelling waves arising from an epileptogenic source
Diamond, J. M., Withers, C. P., Chapeton, J. I., et al. (2023) · Brain · doi:10.1093/brain/awad015 · Cited by 49
An Ultrascalable Solution to Large-scale Neural Tissue Simulation
Kozloski, J., & Wagner, J. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00015 · Cited by 50
“…the physiological and anatomical constraints imposed by real neural tissue (Markram, 2006), where synapse densities may approach one per cubic micron (Braitenberg and Schuz, 1998), and synaptic counts per postsynaptic neuron easily achieve 10 5 –10 6 (for example in neocortex and cerebellum).”
BackgroundHigh-Amplitude Positive Spikes Recorded Extracellularly in Cat Visual Cortex
Gold, C., Girardin, C. C., Martin, K. A. C., & Koch, C. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91365.2008 · Cited by 53
Garagnani, M., & Friedemann Pulvermüller, A. (2013) · Brain and Language · doi:10.1016/j.bandl.2013.02.001 · Cited by 54
“1E) and area-specific inhibition are also implemented, realising, respectively, local and global competition mechanisms (Duncan, 2006) and preventing activation from falling into non-physiological states (Braitenberg & Schüz, 1998).”
BackgroundInfluentialKarbowski, J. (2003) · Journal of Computational Neuroscience · doi:10.1023/a:1027467911225 · Cited by 48
“(4.3) scales in first power with Vg (Schüz and Demianenko, 1995; Braitenberg and Schüz, 1998).”
BackgroundMethodInfluentialYger, P., El‐Boustani, S., Alain Destexhe, A., & Frégnac, Y. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0310-z · Cited by 47
Santuy, A., Rodríguez, J., DeFelipe, J., & Merchán-Pérez, Á. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1470-7 · Cited by 49
“…transmission electron microscopy, in which a percentage of asymmetric and symmetric synapses between 80–95% and 5–20%, respectively were reported (Beaulieu and Colonnier 1985; Braitenberg and Schüz 1998; DeFelipe 2011; DeFelipe and Fariñas 1992; Micheva and Beaulieu 1996; Schüz and Palm 1989).”
Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture
Doborjeh, Z., Kasabov, N. K., Doborjeh, M., & Sumich, A. L. (2018) · Scientific Reports · doi:10.1038/s41598-018-27169-8 · Cited by 52
“The mapped SNN models were initialised using the small-world connectivity rule 41,42 in which a probability of a neuron to be connected to another neuron depends on the distance between the two neurons, the larger the distance – the smaller the probability.”
MethodMcBride, J. C., Zhao, X., Munro, N. B., et al. (2013) · Annals of Biomedical Engineering · doi:10.1007/s10439-013-0788-4 · Cited by 48
Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology
Fingelkurts, A. A., & Fingelkurts, A. A. (2010) · The Open Neuroimaging Journal · doi:10.2174/1874440001004010130 · Cited by 46
Think Global, Act Local; Projectome Estimation with BlueMatter
Sherbondy, A. J., Dougherty, R. F., Ananthanarayanan, R., Modha, D. S., & Wandell, B. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_106 · Cited by 48
“The white matter of the human brain comprises more than 150km of long-range myelinated connections [1].”
BackgroundModelling concrete and abstract concepts using brain-constrained deep neural networks
Henningsen‐Schomers, M. R., & Friedemann Pulvermüller, A. (2021) · Psychological Research · doi:10.1007/s00426-021-01591-6 · Cited by 50
“This produces sparse, patchy and topographic connectivity, as typically found in the mammalian cortex (Amir et al., 1993; Braitenberg & Schüz, 1998; Douglas & Martin, 2004; Kaas, 1997).”
BackgroundUniformity, specificity and variability of corticocortical connectivity
Hilgetag, C. C., & Grant, S. (2000) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2000.0546 · Cited by 47
Stroeve, S., & Gielen, S. C. A. M. (2001) · Neural Computation · doi:10.1162/089976601750399281 · Cited by 47
Topographic numerosity maps cover subitizing and estimation ranges
Cai, Y., Hofstetter, S., van Dijk, J. A., Zuiderbaan, W., van der Zwaag, W., Harvey, B. M., & Dumoulin, S. O. (2021) · Nature Communications · doi:10.1038/s41467-021-23785-7 · Cited by 47
Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control
Lim, S., & Goldman, M. S. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4602-13.2014 · Cited by 44
“…(Melchitzky et al., 2001) or large basket cells (Markram et al., 2004), the neural substrate for widespread inhibition in memory circuits remains unclear because inhibitory projections are typically shorter ranged than excitatory projections (Braitenberg and Schüz, 1998; Douglas and Martin, 2004).”
BackgroundHippocampal networks on reliable patterned substrates
Boehler, M. D., Leondopulos, S. S., Wheeler, B. C., & Brewer, G. J. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.09.020 · Cited by 46
Ramaswamy, S., Hill, S., King, J. G., Schürmann, F., Wang, Y., & Markram, H. (2011) · The Journal of Physiology · doi:10.1113/jphysiol.2011.219576 · Cited by 42
“Such a high variability for potential synaptic contacts arising through axo-dendritic touches in reconstructed neurons has been previously reported (Braitenberg & Schüz, 1998; Fares & Stepanyants, 2009).”
BackgroundAlain Destexhe, A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.002 · Cited by 47
“The vast majority of synapses arise from corticocortical input, either from local axon collaterals or from long-range cortico-cortical fibers [21].”
BackgroundOperational Architectonics Methodology for EEG Analysis: Theory and Results
Fingelkurts, A. A., & Fingelkurts, A. A. (2013) · Neuromethods · doi:10.1007/7657_2013_60 · Cited by 43
Ramirez, R., Ahmed, O. J., Cash, S. S., Wayne, C. E., & Kramer, M. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004065 · Cited by 43
Representations in auditory cortex
Hromádka, T., & Zador, A. M. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.07.009 · Cited by 47
“Subthreshold responses reflect synchronized cortical input Cortical neurons typically receive input from thousands of other cortical neurons [20,21].”
BackgroundA developmental algorithm for ocular–motor coordination
Chao, F., Lee, M. H., & Lee, J. J. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.08.002 · Cited by 44
“The simplicity of the method is important in this regard, as the requirement for fast performance, in both saccading and learning, rules out complex computations and the speed of neural processes limits the number of steps that can be involved [35, 5].”
BackgroundMultifractal Dynamic Functional Connectivity in the Resting-State Brain
Rácz, F. S., Stylianou, O., Mukli, P., & Eke, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01704 · Cited by 49
“Also, the frequency range of network oscillations is constrained by the networks size because most neuronal connections are local and thus yield high-frequency oscillations (Braitenberg and Schüz, 2013).”
BackgroundTomasello, R., Wennekers, T., Garagnani, M., & Friedemann Pulvermüller, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-39864-1 · Cited by 45
“…connectivity: a sparse, random and initially weak connectivity was implemented locally, along with a neighbourhood bias towards close-by links 61,62 ; (v) Between-area connectivity based on neurophysiological principles and motivated by neuroanatomical evidence 46,47 further explained below;…”
Elevated metabolites within dorsolateral prefrontal cortex in rapid cycling bipolar disorder
Michael, N., Erfurth, A., & Pfleiderer, B. (2009) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2009.01.002 · Cited by 44
“Because up to 80% of cortical neurons are glutamatergic (Braitenberg and Schüz, 1998) and the glutamate/glutamine (Glx) signal is mainly dominated by the intracellular transmitter pool (Erecinska and Silver,1990), Glx probably constitutes a suitablemarker of neuronal activity of cortical, mainly…”
BackgroundCheetham, C. E. J., Hammond, M. S. L., McFarlane, R., & Finnerty, G. T. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2974-08.2008 · Cited by 46
“Axon length in RoPs is a poor predictor of local excitatory connectivity and synapse number Many studies of neocortical circuitry based on reconstructions of single neurons have used a working hypothesis commonly termed “Peters’ rule,” which states that the probability of synapse formation between an axon and a dendri…”
BackgroundMethodInfluentialBoucsein, C., Nawrot, M. P., Schnepel, P., & Aertsen, A. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00031 · Cited by 44
Does the brain behave like a (complex) network? I. Dynamics
Papo, D., & Buldú, J. M. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.006 · Cited by 44
Henderson, J. A., & Robinson, P. A. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0151 · Cited by 43
Taga, G., Watanabe, H., & Homae, F. (2011) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2011.0238 · Cited by 43
The case of CAUSE: neurobiological mechanisms for grounding an abstract concept
Friedemann Pulvermüller, A. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0129 · Cited by 42
The Problem of Non-Shared Environment in Behavioral Genetics
Tikhodeyev, O. N., & Shcherbakova, O. (2019) · Behavior Genetics · doi:10.1007/s10519-019-09950-1 · Cited by 40
“The number and location of these connections are under complex molecular genetic control, but the role of stochastic events is also significant (Braitenberg and Schüz 1998; Feldmeyer and Lübke 2010; Kaiser et al. 2009; Merchán-Pérez et al. 2014).”
BackgroundMechanisms of cache retrieval in long-term hoarding birds
Brodin, A. (2005) · Journal of Ethology · doi:10.1007/s10164-005-0147-5 · Cited by 43
“In reality the effect of such a difference will probably be counteracted by an increase with brain size in the number of synapses per neuron (Braitenberg and Schüz 1998).”
BackgroundMemorization and Association on a Realistic Neural Model
Valiant, L. G. (2005) · Neural Computation · doi:10.1162/0899766053019890 · Cited by 42
“The number of neurons in mouse cortex has been estimated to be 1.6 x 10 7 , while the corresponding estimate is in the region of 10 10 for humans (Braitenberg and Schuz, 1998).”
BackgroundInfluentialA Theory of Loop Formation and Elimination By Spike Timing-Dependent Plasticity
Kozloski (2010) · Frontiers in Neural Circuits · doi:10.3389/fncir.2010.00007 · Cited by 44
“Connections between individual neurons in the brain are constrained first by the spatial distribution of axons and dendrites within the neuropil [1][2].”
Metadata
- Authors
- Braitenberg, V., & Schüz, A.
- Affiliations
- Max Planck Society (DE)
- Venue
- Springer eBooks
- Publisher
- Springer Berlin Heidelberg
- Published
- 1998
- OpenAlex
- W1603661052
- Cited by
- 1,178
- References
- 0
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 38 · 2023: 62 · 2022: 38 · 2021: 54 · 2020: 34 · 2019: 57 · 2018: 49 · 2017: 44 · 2016: 42 · 2015: 57 · 2014: 53 · 2013: 51 · 2012: 64
- FWCI
- 4.64
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- cerebral cortex, neuronal connectivity, cortical networks, mouse cortex, cortical geometry, corticocortical connections
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