Cited by 1,178Functional Brain Connectivity StudiesNeural dynamics and brain functionAdvanced Neuroimaging Techniques and Applications

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Cortex: Statistics and Geometry of Neuronal Connectivity

Braitenberg, Valentino, Schüz, Almut·1998·Springer eBooks

DOI 10.1007/978-3-662-03733-1

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“Furthermore, histological and electrophysiological works have suggested that excitatory synapses at apical dendrites of cortical pyramidal cells mediate a large proportion of intra-cortical signal exchange ( Braitenberg and Schuz, 1998 ).”
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“The synaptic connectivity ranges that have been used are contained within reported ranges -of 40 µ m to 2mm-of synaptic connectivity measurements (Braitenberg and Schuz, 1998; Linden et al., 2011; Peyrache et al., 2012).”
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“Synapses projecting to the cortex have the same chance per target neuron of terminating on an inhibitory or excitatory neuron (random connectivity) (Braitenberg and Schüz, 1998; Wright and Liley, 1996).”
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Background
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Thivierge, J., Comas, R., & Longtin, A. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00022 · Cited by 8
“The probability of contact between two neocortical excitatory cells that are 0.2–0.3 mm apart is estimated to be p < 0.1, and between two such cells that are more than 1 mm from each other, p < 0.01 (Braitenberg and Schuz, 1998; Song et al., 2005).”
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“Interestingly, however, there was a close similarity in the proportions of the different target types in the neuropil surrounding the labeled collateral in layer 5, which is what Peters’ Rule of connectivity would predict (Braitenberg and Schüz 1991, 2013).”
Background
Neural Field Theory of Evoked Response Potentials With Attentional Gain Dynamics
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“…ν ie = ν ee , ν ii = ν ei , and ν is = es because the number of cortical synapses is closely proportional to the numbers of source and target neurons (Wright and Liley, 1996; Robinson et al., 1997; Braitenberg and Schüz, 1998), assuming the strength of synapses is determined by the source neurons.”
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“Since the majority of synapses in the cortex of mammals is excitatory (about 80–90%; [84,85]), the weights w ij are assumed to be positive, which implies that the steady-state average values of r i are all positive.”
Background
Can human brain connectivity explain verbal working memory?
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Background
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“The average number of neurites per neuron was important for assessing neuronal and local cortex functionality.”
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Method
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“It is therefore an intermediate measurement between single-unit electrophysiology and LFP or fMRI BOLD signals, both of which sum over 1–4 mm of cortical volume (Berens et al., 2008; Braitenberg & Schüz, 1998; Kajikawa & Schroeder, 2011; Katzner et al., 2009).”
Background
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Background
Layer-dependent stability of intracortical recordings and neuronal cell loss
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“Cells in the cortex are densely packed; for example, 1 mm3 of mouse cortex contains approximately 200,000 neurons (Braitenberg and Schüz, 2013).”
Background
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Background
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“Given the remarkable heterogeneity of the mammalian neocortex (Braitenberg and Schüz 1998), neuronal pattern separation may be found across several regions, particularly where low signal correlations are present, as may be expected in several areas of cortex (Brody et al.”
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“(assuming 8,000 synapse/neuron) may exist in a cubic millimeter of cortex tissue [ 3 ].”
Background
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Self-citation
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BackgroundInfluential
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Background
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BackgroundMethod
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Background
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Background
Mode-locking dynamics of corticothalamic system responses to periodic external stimuli
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“1995), or only general statistics are accessible (Braitenberg and Schüz 1998).”
BackgroundInfluential
Performance of beamformers on EEG source reconstruction
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“Specific anatomical and dynamical connectivity patterns are an essential ingredient of healthy brain functioning (Varela et al. 2001; Braitenberg and Sch¨uz 2013).”
Background
Discrete spectral eigenmode-resonance network of brain dynamics and connectivity
Robinson, P. A. (2021) · Physical review. E · doi:10.1103/physreve.104.034411 · Cited by 6
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“This structural noise, similar to the notion of “quenched disorder” in physics [28,29], is also present in the cerebral cortex which contains cells of very diverse shapes and sizes [30].”
Background
Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST
Schmitt, F. J., Rostami, V., & Nawrot, M. P. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.941696 · Cited by 7
“We follow the widely used assumption that 80% of neurons in the neocortex are excitatory while 20% are inhibitory to build the network model, which is based on the statistical work in the mouse neocortex by Braitenberg and Schüz (1998).”
Method
Quantum computing architectures with signaling and control mimicking biological processes
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“Maps of these zones have already been surveyed [44].”
Background
Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia
Юрьевич, Ш. Ю., Efremov, A., Kuptsova, A., Wennekers, T., Gutkin, B. S., & Garagnani, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-41922-8 · Cited by 9
Role of Neuronal Mitochondrial Metabolic Phenotype in Pathogenesis of ALS
Panov, A. V., Steuerwald, N., Андреевич, В. В., Dambinova, S. A., & Herbert, L. (2012) · InTech eBooks · doi:10.5772/30418 · Cited by 6
Action potentials recorded from bundles of very thin, gray matter axons in rat cerebellar slices using a grease-gap method
Palani, D., Pękala, D., Baginskas, A., Szkudlarek, H. J., & Raastad, M. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.05.005 · Cited by 6
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Analyzing composability in a sparse encoding model of memorization and association
Beal, J., & Knight, T. F. (2008) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2008.4640826 · Cited by 6
“6×10(7) and the number of neurons in human cortex is estimated to be approximately 10(10)[4], their degree—the number of neurons with which each neuron synapses—is estimated to be much smaller, approximately 7800 in mouse cortex and 24,000-80,000 in human cortex[1].”
Background
A Neuronal Network Model for Simulating the Effects of Repetitive Transcranial Magnetic Stimulation on Local Field Potential Power Spectra
Bey, A., Leue, S., & Wienbruch, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049097 · Cited by 8
“Third, we fit the model with experimentally validated parameter values [22,23].”
BackgroundMethod
Localizing Cortical Computations during Visual Selection
Rainer, G. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.002 · Cited by 6
Neuroheuristics of Decision Making: From Neuronal Activity to EEG
Villa, A. E. P., Missonnier, P., & Lintas, A. (2011) · Intelligent systems reference library · doi:10.1007/978-3-642-24647-0_7 · Cited by 6
“The connectivity in the brain is rather sparse and the chances of re-entrant connections within a cell assembly may be very small, the activity would essentially propagate in a feedforward manner [2, 17].”
Background
Nonlinear harmonic generation in the corticothalamic system
Ferdousi, M., Babaie, T., & Robinson, P. A. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.10.013 · Cited by 6
Voltage Imaging of Cortical Oscillations in Layer 1 with Two-Photon Microscopy
Dalphin, N., Dorgans, K., Khaskin, E., & Kühn, B. (2020) · eNeuro · doi:10.1523/eneuro.0274-19.2020 · Cited by 6
“Dendrites in the upper cortex make up 23% of the cortical surface area (Williams et al., 1980; Braitenberg and Schüz, 1998).”
Background
Nonlocal Mechanism for Synchronization of Time Delay Networks
Kanter, I., Kopelowitz, E., Vardi, R., Zigzag, M., Cohen, D., & Kinzel, W. (2011) · Journal of Statistical Physics · doi:10.1007/s10955-011-0361-1 · Cited by 6
Connectivity graphs and the performance of sparse associative memory models
Chen, W., Adams, R., Calcraft, L., Steuber, V., & Davey, N. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634183 · Cited by 6
Associative memory as a Bayesian building block
Zhu, S. (2008) · doi:10.6083/m4pz56sc · Cited by 6
APPROACHES TO EFFICIENT SIMULATION WITH SPIKING NEURAL NETWORKS
Connolly, C. G., Marian, I., & Reilly, R. B. (2004) · doi:10.1142/9789812702784_0022 · Cited by 6
Synaptogenèses normales, pathologiques et amendables dans le cortex cérébral
Bourgeois, J. (2008) · PSN · doi:10.1007/s11836-008-0065-z · Cited by 6
“Dans 1 mm(3) de cortex cérébral de souris adulte, on dénombre environ 90 000 cellules neuronales, 460 m de fines branches dendritiques et 3 à 4 km de fines branches axonales interconnectés par 300 à 600 millions de contacts synaptiques [12].”
Background
Uniformity and specificity of long-range corticocortical connections in the visual cortex of the cat
Hilgetag, C. C., & Grant, S. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00429-5 · Cited by 6
A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework
Carriere, M., Dobler, F. R., Pleßer, H. E., Feledyn, A., Tomasello, R., Wennekers, T., & Friedemann Pulvermüller, A. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10415-5 · Cited by 5
“…the model therefore samples the typical activation pattern of excitatory neurons in a small neighbourhood (of the order of perhaps 100–200 μm in radius), and the corresponding local inhibitory cell represents the collective response of nearby interneurons (Fuster 1997; Braitenberg and Schüz 1998).”
Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron
Kobayashi, R., He, J., & Lánský, P. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00059 · Cited by 5
“The number of presynaptic neurons can be estimated to be 5000 for excitatory neurons and 1000 for inhibitory neurons (Braitenberg and Schuz, 1998).”
Background
Systematic analysis of neuronal wiring of the rodent deep cerebellar nuclei reveals differences reflecting adaptations at the neuronal circuit and internuclear levels
Hamodeh, S., Baizer, J. S., Sugihara, I., & Sultan, F. R. (2014) · The Journal of Comparative Neurology · doi:10.1002/cne.23545 · Cited by 6
A continuum-field model of visual cortex stimulus-driven behaviour: emergent oscillations and coherence fields
Sabatini, S. P., Solari, F., & Secchi, L. (2004) · Neurocomputing · doi:10.1016/j.neucom.2003.10.016 · Cited by 6
Genes, Brains, Minds: The Human Complex
Rolston, H. (2005) · Palgrave Macmillan US eBooks · doi:10.1057/9781403979230_2 · Cited by 5
The Largest Cognitive Systems Will be Optoelectronic
Shainline, J. M. (2018) · IEEE International Conference on Rebooting Computing (ICRC) · doi:10.1109/icrc.2018.8638599 · Cited by 7
“ing an estimate of w(p) = 1:9 10 5 m. Here w refers to the size of a synapse, not a neuron, as discussed above. If we assume each of the 1:6 1010 neurons in the human cerebral cortex has 104 synapses [52], we ﬁnd w(b) = 2:4 10 8 m. A biological synapse is 1000 times smaller than a superconducting optoelectronic synaps…”
Background

Authors: Braitenberg, V., & Schüz, A.
Affiliations: Max Planck Society (DE)
Venue: Springer eBooks
Publisher: Springer Berlin Heidelberg
Published: 1998
DOI: 10.1007/978-3-662-03733-1
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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