The DOI matches its Crossref record.
Cortex: Statistics and Geometry of Neuronal Connectivity
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cited by
Sorted by citations
Items 601–700 of 1,000
Noninvasive three-dimensional optoacoustic localization microangiography of deep tissuesPreprint
Deán‐Ben, X. L., Robin, J., Ni, R., & Razansky, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00372 · Cited by 14
Henderson, J. A., Aquino, K. M., & Robinson, P. A. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100103 · Cited by 15
Khatri, R., Machart, P., & Bonn, S. (2024) · Genome biology · doi:10.1186/s13059-024-03251-5 · Cited by 14
Epileptic seizures from abnormal networks: Why some seizures defy predictability
Anderson, W. S., Azhar, F., Kudela, P., Bergey, G. K., & Franaszczuk, P. J. (2011) · Epilepsy Research · doi:10.1016/j.eplepsyres.2011.11.006 · Cited by 15
“Even in the non-epileptic brain, excitatory connections predominate with 80—90% of synapses being excitatory (Braitenberg and Schüz, 1998).”
BackgroundAćimović, J., Mäki‐Marttunen, T., & Linne, M. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2015.00076 · Cited by 13
“A more realistic rule would require that each dendritic segment connects to at most one among all the proximal segments of all the axons, this may better reflect the connectivity in cortical tissue (Braitenberg and Schüz, 1998) and also reduce the total number of potential synapses per neuron.”
BackgroundInfluentialJittering waves in rings of pulse oscillators
Klinshov, V. V., Shchapin, D. S., Yanchuk, S., et al. (2016) · Physical review. E · doi:10.1103/physreve.94.012206 · Cited by 15
Expressive power of first-order recurrent neural networks determined by their attractor dynamics
Cabessa, J., & Villa, A. E. P. (2016) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2016.04.006 · Cited by 15
“In addition, the presence of recurrent connections within large neural circuits indicates that re-entrant activity through chains of neurons should represent a major hallmark of brain circuits [5].”
Contrast response in a comprehensive network model of macaque V1
Chariker, L., Shapley, R. M., & Young, L.-S. (2020) · Journal of Vision · doi:10.1167/jov.20.4.16 · Cited by 15
“Recurrent circuitry is a feature of cerebral cortex (Braitenberg & Schüz, 1998; Douglas & Martin, 1991, 2007; Thomson et al., 2002) that influences many functions of the cortex.”
BackgroundSpiegler, A., Abadchi, J. K., Mohajerani, M. H., & Jirsa, V. (2020) · Network Neuroscience · doi:10.1162/netn_a_00152 · Cited by 15
“The characteristic distance of short-range SC within plausible bounds (Braitenberg & Schüz, 1998) 3.”
BackgroundInfluentialPhysiological Foundations of Neural Signals
Quiroga, R. Q., & Panzeri, S. (2013) · CRC Press eBooks · doi:10.1201/b14756-5 · Cited by 15
DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale
Cheng, S., Wang, X., Liu, Y., Su, L., Quan, T., Li, N., Yin, F.-F., Xiong, F., Liu, X., Luo, Q., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00025 · Cited by 15
“Therefore, identification of axonal boutons of individual neurons is critical for interpreting the patterns of neural circuit wiring schemes, as boutons indicate contact sites of individual neurons and reveal how neural circuits are wired (Braitenberg and Schüz, 1998; Lichtman and Denk, 2011).”
BackgroundDeFiNe: an optimisation-based method for robust disentangling of filamentous networks
Breuer, D., & Nikoloski, Z. (2015) · MPG.PuRe (Max Planck Society) · Cited by 15
Operant conditioning of left-hemispheric slow cortical potentials and its effect on word processing
Friedemann Pulvermüller, A., Mohr, B., Schleichert, H., & Veit, R. (2000) · Biological Psychology · doi:10.1016/s0301-0511(00)00046-6 · Cited by 16
Simulation of associative neural networks
Zhu, S., & Hammerstrom, D. W. (2003) · International Conference on Neural Information Processing · doi:10.1109/iconip.2002.1198953 · Cited by 15
“According to Braitenberg [6], there are two kinds of connections in cortex: metric and ametric.”
BackgroundComputational neurobiology meets semiconductor engineering
Hammerstrom, D. W. (2002) · Proceedings/Proceedings - International Symposium on Multiple-Valued Logic · doi:10.1109/ismvl.2000.848593 · Cited by 15
“Braitenberg [ 28 ] postulates two general connectivity systems in cortex: “metric” (high density connections to physically local cells, based on actual two-dimensional layout), and “ametric” (low density point-to-point connections to all large groups of densely connected cells).”
BackgroundCardona, A., Saalfeld, S., Tomančák, P., & Hartenstein, V. (2009) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2009.74.037 · Cited by 17
“In the mammalian cortex, synaptic density is toward the lower end of this range, at 0.72/µm 3 (Braitenberg and Schüz 1998).”
InfluentialPaus, T. (2009) · Wiley eBooks · doi:10.1002/9780470479193.adlpsy001005 · Cited by 30
A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm
Bastiaens, S. P., Momi, D., & Griffiths, J. D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012926 · Cited by 15
Statistics and geometry of orientation selectivity in primary visual cortex
Sadeh, S., & Rotter, S. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0576-0 · Cited by 14
“1.4 Geometry of cortex and organization of orientation There is a theory of OS and its organization based on the geometry of cortex (Braitenberg 1985, 1992; Braitenberg and Braitenberg 1979; Braitenberg and Schüz 1998).”
BackgroundInfluentialA Morpho-Density Approach to Estimating Neural Connectivity
McAssey, M. P., Bijma, F., Tarigan, B., van Pelt, J., Arjen Van Ooyen, B., & de Gunst, M. C. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086526 · Cited by 14
Coupling of glutamate and glucose uptake in cultured Bergmann glial cells
Méndez-Flores, O. G., Hernández‐Kelly, L. C., Suárez‐Pozos, E., Najimi, M., & Ortega, A. (2016) · Neurochemistry International · doi:10.1016/j.neuint.2016.05.001 · Cited by 14
Voltage Imaging with ANNINE Dyes and Two-Photon Microscopy
Roome, C. J., & Kühn, B. (2019) · Neuromethods · doi:10.1007/978-1-4939-9702-2_13 · Cited by 15
“, the surface ratio between dendrites, axons, and astrocytes (see for example [45]) help to estimate the expected voltage signal [29].”
BackgroundState-Dependent Changes in Brain Glycogen Metabolism
DiNuzzo, M., Walls, A. B., Öz, G., Seaquist, E. R., Waagepetersen, H. S., Bak, L. K., Nedergaard, M., & Schousboe, A. (2019) · Advances in neurobiology · doi:10.1007/978-3-030-27480-1_9 · Cited by 13
Noise in Neurons and Other Constraints
Faisal, A. A. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_8 · Cited by 15
Sizemore, R. J., Reynolds, J. N. J., & Oorschot, D. E. (2010) · Journal of Anatomy · doi:10.1111/j.1469-7580.2010.01264.x · Cited by 14
“The second aim was to compare these data with other m2- labelled and unlabelled striatal interneuronal dendrites in the rat, and with published synaptic data for dendrites in the mouse and cat cerebral cortex (Anderson et al. 1994; Braitenberg & Schüz, 1998).”
ResultInfluentialBifurcations and state changes in the human alpha rhythm: Theory and experiment
Liley, D. T. J., Bojak, I., Dafilis, M. P., VEEN, L. V., Frascoli, F., & Foster, B. L. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0796-7_6 · Cited by 15
“Averaged over the whole of cortex, less than 2‐3% of all synapses can be attributed to thalamocortical projections [ 10 ].”
BackgroundStereology of Neural Connections: An Overview
Avendaño, C. (2006) · Springer eBooks · doi:10.1007/0-387-28942-9_16 · Cited by 14
Mean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons
Sadeh, S., Cardanobile, S., & Rotter, S. (2014) · SpringerPlus · doi:10.1186/2193-1801-3-148 · Cited by 14
“Our model consists of a recurrent network of 12 500 leaky integrate-and-fire (LIF) neurons, 80% excitatory and 20% inhibitory (Braitenberg and Schüz 1998).”
(2010) · Cited by 15
The effects of dynamical synapses on firing rate activity: a spiking neural network model
Khalil, R., Moftah, M. Z., & Moustafa, A. A. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13712 · Cited by 15
Luboeinski, J., & Tchumatchenko, T. (2020) · Network Neuroscience · doi:10.1162/netn_a_00154 · Cited by 14
An abstract model of a cortical hypercolumn
Curuklu, B., & Lansner, A. (2004) · International Conference on Neural Information Processing · doi:10.1109/iconip.2002.1202135 · Cited by 14
Statistical analysis of spatially embedded networks: From grid to random node positions
Voges, N., Aertsen, A., & Rotter, S. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.126 · Cited by 14
“The architecture of non-local cortical networks has a much richer structure, and in particular, it has a spatial dimension: Cortical wiring is, to some extent, distance-dependent [5–8], and it comprises both local and long-range connections [3].”
Sandberg, A., Bostrom, N., & Martin, J. (2008) · Cited by 20
Brain Circuit Implementation: High-Precision Computation from Low-Precision Components
Granger, R. (2005) · The MIT Press eBooks · doi:10.7551/mitpress/6858.003.0018 · Cited by 14
Nonlinear wave-wave interactions in the brain
Ferdousi, M., Babaie-Janvier, T., & Robinson, P. A. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2020.110308 · Cited by 13
“We assume that the number of connections to and between cortical populations is proportional to the number of synapses available (Braitenberg and Schüz, 1998, Liley and Wright, 1994, Wright and Liley, 1994).”
MethodInteracting Hawkes processes with multiplicative inhibition
Duval, C., Luçon, É., & Pouzat, C. (2022) · Stochastic Processes and their Applications · doi:10.1016/j.spa.2022.02.008 · Cited by 13
“In ’real life’, our μA and μB account for the following two features: i) the weaker one, the intrinsic properties of the neurons (that make them more or less prone to spike spontaneously) [37]; ii) the stronger one, the excitatory inputs from the other brain regions (other neocortical area and mostly thalamic inputs […”
BackgroundPhase Transition for Infinite Systems of Spiking Neurons
Ferrari, P. A., Galves, A., Grigorescu, I., et al. (2018) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 13
Error correction and improved precision of spike timing in converging cortical networks
Levi, A., Spivak, L., Sloin, H. E., Someck, S., & Stark, E. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111383 · Cited by 13
Simulating cortical network activity states constrained by intracellular recordings
Alvarez, F. P., & Destexhe, A. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.01.057 · Cited by 13
“DiLerent network topologies were studied, but preserving the ratio 4:1 between PYs and INs characterized for the neocortex [1].”
BackgroundUnderstanding Neural Population Coding: Information Theoretic Insights from the Auditory System
Onken, A., Karunasekara, P. P. C. R., Kayser, C., & Panzeri, S. (2014) · Advances in Neuroscience · doi:10.1155/2014/907851 · Cited by 13
Felch, A., & Granger, R. (2007) · Brain Research · doi:10.1016/j.brainres.2007.06.044 · Cited by 13
“…first seem difficult to achieve, but may approximate the actual, surprisingly regular, distributions of synapses in real brain circuits (see, e.g., Braitenberg and Schüz, 1998); close approximations to hypergeometric distributions may readily be achieved via initial overgeneration of synaptic…”
InfluentialA Review of MR Spectroscopy Studies of Pediatric Bipolar Disorder
Kondo, D. G., Hellem, T., Shi, X., Sung, Y. H., Prescot, A. P., Kim, T.-E., Huber, R. S., Forrest, L. N., & Renshaw, P. F. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3844 · Cited by 13
Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields
Yildiz, I. B., Mesgarani, N., & Denève, S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4648-15.2016 · Cited by 11
“However, in addition to thalamic inputs, cortical neurons receive both excitatory and inhibitory intracortical inputs, strongly shaping their spontaneous and sensory-evoked activities (Lamme et al., 1998; Braitenberg and Schüz, 1998; Lee and Winer, 2008).”
BackgroundNeural Field Theory of Corticothalamic Prediction With Control Systems Analysis
Babaie, T., & Robinson, P. A. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00334 · Cited by 13
Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks
Histed, M. H. (2018) · eNeuro · doi:10.1523/eneuro.0356-17.2018 · Cited by 13
“Single axons from the thalamus often ramify across several hundred microns of the cortex (Braitenberg and Schüz, 2001; Garraghty and Sur, 1990), and thalamic axons projecting to the visual cortex can make synapses on dozens of excitatory cortical cells (Freund et al.”
BackgroundDouglas, R. J., & Martin, K. A. C. (2007) · Brain Research Reviews · doi:10.1016/j.brainresrev.2007.04.011 · Cited by 12
“Estimates of the length of the local axons of an average cortical cell, based on the volume of neuropil occupied by axon, predicted that each cortical cell would have a local axonal plexus that was a staggering 40 mm long and formed several thousand synapses (Braitenberg and Schüz, 1998).”
BackgroundStructure induces computational function in networks with diverse types of spiking neuronsPreprint
Stöckl, C., Lang, D., & Maass, W. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.18.444689 · Cited by 16
“The number of synapses per neuron was estimated in (Braitenberg and Sch¨uz, 2013) to be 7777, and the total length of axons per neuron was estimated to be 4 .”
MethodTrensch, G., & Morrison, A. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.884033 · Cited by 12
“This is in agreement with a connection probability value of approx. ǫ = 0.1 observed in Braitenberg and Schüz (1998).”
BackgroundInfluentialComputational constraints on the associative recall of spatial scenes
Ryom, K. I., Stendardi, D., Ciaramelli, E., & Treves, A. (2023) · Hippocampus · doi:10.1002/hipo.23511 · Cited by 11
“As a memory device, therefore, it operates by self-organization, without instructions or extra ingredients, as envisaged by Braitenberg [17].”
BackgroundSørensen, M., Andersen, J. V., Bjerring, P. N., & Vilstrup, H. (2024) · Metabolic Brain Disease · doi:10.1007/s11011-024-01473-x · Cited by 13
How reliable is the connectivity in cortical neural networks?
Faisal, A. A., Laughlin, S. B., & White, J. A. (2003) · doi:10.1109/ijcnn.2002.1007767 · Cited by 12
“In the cortex, pyramidal cells massively use unmyelinated axon collaterals (average diameter m) for (local) communication (average of cm collateral per pyramidal cell) which results in very high cabling densities of km/mm [5].”
BackgroundNeutral Stability, Rate Propagation, and Critical Branching in Feedforward Networks
Cayco-Gajic, N. A., & Shea‐Brown, E. (2013) · Neural Computation · doi:10.1162/neco_a_00461 · Cited by 12
Sun, J., Sathasivam, S., & Ali, M. K. M. (2022) · IEEE Access · doi:10.1109/access.2022.3204821 · Cited by 13
An Optimized Structure-Function Design Principle Underlies Efficient Signaling Dynamics in Neurons
Puppo, F., George, V. K., & Silva, G. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-28527-2 · Cited by 13
Microscopic theory of intrinsic timescales in spiking neural networks
van Meegen, A., & van Albada, S. J. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043077 · Cited by 11
Insular Cell Integrity Markers Linked to Weight Concern in Anorexia Nervosa—An MR-Spectroscopy Study
Maier, S. J., Nickel, K., Perlov, E., Kukies, A., Zeeck, A., van Elst, L. T., Endres, D., Spieler, D., Holovics, L., Hartmann, A., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051292 · Cited by 11
“Glu is the primary excitatory neurotransmitter in the human brain (~85% of all synapses in the central nervous system are glutamatergic) [14] and has been implicated in different psychiatric disorders associated with AN, including anxiety disorders, depression, and obsessive-compulsive disorder [15–18].”
BackgroundMetastable spiking networks in the replica-mean-field limit
Yu, L., & Taillefumier, T. O. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010215 · Cited by 16
“Owing to the high degree of connectivity observed in cortical circuits, a prevailing hypothesis is that a neuron’s response is primarily shaped by its mean input drive, computed as an average over many equally contributing synapses [8].”
BackgroundPrimavera, B. A., & Shainline, J. M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.732368 · Cited by 11
Quantum propensities in the brain cortex and free will
Georgiev, D. D. (2021) · Biosystems · doi:10.1016/j.biosystems.2021.104474 · Cited by 12
“Cortical pyramidal neurons form on average 7500 synapses onto target neurons [86], where each electrically excited axonal bouton of a cortical pyramidal neuron is almost twice more likely to fail than succeed in releasing neurotransmitter [62, 63].”
BackgroundBrain Embodiment of Category-Specific Semantic Memory Circuits
Friedemann Pulvermüller, A. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511805837.004 · Cited by 12
Evoked response activity eigenmode analysis in a convoluted cortex via neural field theory
Mukta, K. N., Robinson, P. A., Pagès, J. C., et al. (2020) · Physical review. E · doi:10.1103/physreve.102.062303 · Cited by 12
Towards Cortex Sized Artificial Nervous Systems
Johansson, C., & Lansner, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30132-5_129 · Cited by 12
“of synapses per neuron in mouse [ 10 ], cat [13], and man [14] is about 8·10”
BackgroundInfluentialDeep tissue optoacoustic localization microangiographyPreprint
Deán‐Ben, X. L., Robin, J., Ni, R., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-536048/v1 · Cited by 12
A theory for the emergence of neocortical network architecturePreprint
Udvary, D., Harth, P., Macke, J. H., Hege, H., de Kock, C. P. J., Sakmann, B. J., & Oberlaender, M. (2020) · MPG.PuRe (Max Planck Society) · Cited by 12
Heritability of cortical morphology reflects a sensory-fugal plasticity gradientPreprint
Vainik, U., Paquola, C., Wang, X., Zheng, Y., Bernhardt, B. C., Mišić, B., & Dagher, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.03.366419 · Cited by 12
Nguyen, P. T. U., Henningsen‐Schomers, M. R., & Friedemann Pulvermüller, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1048-23.2023 · Cited by 16
Tian, Q., Ngamsombat, C., Lee, H., Berger, D. R., Wu, Y., Fan, Q., Bilgiç, B., Li, Z., Novikov, D. S., Fieremans, E., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121212 · Cited by 12
“These fibers, also known as U-fibers, follow the highly convoluted cortical manifold and are estimated to occupy 240 out of 420 cm 3 of the entire human cortical white matter volume (Schüz and Braitenberg, 2002; Markov et al., 2013; Braitenberg and Schüz, 2013).”
Chapeton, J. I., Gala, R., & Stepanyants, A. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00074 · Cited by 11
“Equations (7) and (8) were solved with custom MatLab code (Theoretical_Results.m of Supplementary Materials) to produce the results shown in Figures 2, 3, and Figure S1.”
BackgroundMethodInfluentialLocalizing Brain Activity from Multiple Distinct Sources via EEG
Dassios, G., Doschoris, M., & Satrazemi, K. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/232747 · Cited by 11
“…25 June 2014; Accepted 31 October 2014; Published 23 November 2014 Academic Editor: Urmila Diwekar Copyright © 2014 George Dassios et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and…”
BackgroundCMOS / CMOL architectures for spiking cortical column
Gao, C., Zaveri, M., & Hammerstrom, D. W. (2008) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634138 · Cited by 11
Global segregation of cortical activity and metastable dynamics
Stratton, P. G., & Wiles, J. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00119 · Cited by 11
“Because up to 99% of cortical connections derive from the cortex (Braitenberg and Schüz, 1998) and only a small fraction of the brain’s energy consumption is used for the processing of external events (Raichle and Mintun, 2006; Zhang and Raichle, 2010), by far the majority of cortical activity…”
BackgroundPhase Transition for Infinite Systems of Spiking Neurons
Ferrari, P. A., Galves, A., Grigorescu, I., et al. (2018) · Journal of Statistical Physics · doi:10.1007/s10955-018-2118-6 · Cited by 11
“But in simpler nervous tissues like the retina neurons are arranged in layers inducing an interaction structure of this type (see, for instance, Braitenberg and Schüz (1998)).”
BackgroundNeural architecture: from cells to circuits
Richards, S. E. V., & Hooser, S. D. V. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00044.2018 · Cited by 12
“Each dendrite is itself only a few micrometers in diameter, but the combined arbor of one cortical pyramidal neuron can span hundreds of micrometers across the cortex in the mouse and, on average, comprises ~3.5 mm of total material (Braitenberg and Schüz 1998).”
BackgroundInfluentialYang, D., & Robinson, P. A. (2019) · Physical review. E · doi:10.1103/physreve.100.032405 · Cited by 12
Neural Field Theory of Corticothalamic Attention With Control System Analysis
Babaie-Janvier, T., & Robinson, P. A. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01240 · Cited by 11
Dąbrowska‐Bouta, B., Sułkowski, G., Sałek, M., Frontczak‐Baniewicz, M., & Strużyńska, L. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063067 · Cited by 11
“It has been reported that about 80–90% of all neurons in the brain are glutamatergic neurons that use glutamate as a neurotransmitter [4].”
BackgroundOptimising the energetic cost of the glutamatergic synapse
Lezmy, J., Harris, J. J., & Attwell, D. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108727 · Cited by 11
A data-informed mean-field approach to mapping of cortical parameter landscapes
Xiao, Z.-C., Lin, K. K., & Young, L.-S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009718 · Cited by 11
Graben, P. B. (2007) · Springer eBooks · doi:10.1007/978-3-540-73159-7_1 · Cited by 12
A chaos synchronization-based dynamic vision model for image segmentation
Azhar, H., Iftekharuddin, K. M., & Kozma, R. (2006) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556416 · Cited by 11
Muly, E. C., Nairn, A. C., Greengard, P., & Rainnie, D. G. (2008) · The Journal of Comparative Neurology · doi:10.1002/cne.21703 · Cited by 12
“Labeled dendrites and spines are over three times more frequently observed than labeled axons or terminals, whereas, in the neuropil, dendrites and spines occupy only 44% more area than axons and terminals (Schuz and Palm, 1989; Braitenburg and Schuz, 1998).”
BackgroundTamura, H., Mori, Y., & Kaneko, H. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00336.2013 · Cited by 11
Holographic Memory: A Novel Model of Information Processing by Neuronal Microcircuits
Redozubov, A. (2017) · Springer series in cognitive and neural systems · doi:10.1007/978-3-319-29674-6_13 · Cited by 11
Hecht-Nielsen, R. (2003) · Springer eBooks · doi:10.1007/978-1-4471-0085-0_4 · Cited by 12
Construction of anatomically correct models of mouse brain networks
McCormick, B. H., Koh, W., Choe, Y., et al. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.01.070 · Cited by 11
Efficient architectures for sparsely-connected high capacity associative memory models
Calcraft, L., Adams, R., & Davey, N. (2007) · Connection Science · doi:10.1080/09540090701396789 · Cited by 11
“Cortical connectivity is of particular interest, as it is thought that one major function of the cortex is to act as a very large associative memory (Braitenberg and Schüz 1998).”
BackgroundInfluentialTomasello, R., Carriere, M., & Friedemann Pulvermüller, A. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108816 · Cited by 11
“…potentials V In in input to the cell; this regulatory mechanism ensures that area (and network) activity is maintained within physiological levels (Braitenberg and Schüz, 1998): Excitatory links within and between (possibly non-adjacent) model areas are established at random and limited to a…”
InfluentialThe effect of graph connectivity on metastability in a stochastic system of spiking neurons
André, M., & Planche, L. (2020) · Stochastic Processes and their Applications · doi:10.1016/j.spa.2020.09.013 · Cited by 10
“As already noticed in [10], the case of the nearest-neighbors interaction is a radical simplification, and it doesn’t apply to the brain structure in general, but this kind of structure can be found in simpler nervous tissue, such as the retina (see [1]).”
BackgroundProcessing of Feature Selectivity in Cortical Networks with Specific Connectivity
Sadeh, S., Claudia Clopath, A., & Rotter, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127547 · Cited by 10
“Our model consists of a recurrent network of N = 5 000 leaky integrateand-fire (LIF) neurons, of which f = 80% are excitatory and 20% are inhibitory [30].”
MethodFunctional mapping of cortical areas with optical imaging
Holthoff, K., Sagnak, E., & Witte, O. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.059 · Cited by 10
“2), the layer with the highest cell density of this area (Braitenberg and Schuetz, 1998).”
BackgroundInterneurons contribute to the hemodynamic/metabolic response to epileptiform discharges
Saillet, S., Quilichini, P., Ghestem, A., Giusiano, B., Ivanov, A., Hitziger, S., Vanzetta, I., Bernard, C., & Bénar, C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00994.2014 · Cited by 10
NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models
Layer, M., Senk, J., Essink, S., et al. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.835657 · Cited by 10
“Biological neuronal networks are composed of large numbers of recurrently connected neurons, with a single cortical neuron typically receiving synaptic inputs from thousands of other neurons (Braitenberg and Schüz, 1998; DeFelipe et al., 2002).”
BackgroundGeometric and functional architecture of visceral sensory microcircuitry
Negishi, Y., & Kawai, Y. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0294-5 · Cited by 10
“This rule states that neurons interconnect in proportion to the neuropil contribution from their dendrites and axonal varicosities (Braitenberg and Schuz 1998), and has been substantiated by theoretical and realistic neurogeometry (Shepherd et al. 2005; Stepanyants and Chklovskii 2005).”
Destexhe, A. (2010) · Science · doi:10.1126/science.1196743 · Cited by 9
Hippocampal Deep Brain Stimulation Reduces Glucose Utilization in the Healthy Rat Brain
Berge, N. V. D., Keereman, V., Vanhove, C., Nieuwenhuyse, B. V., van Mierlo, P., Raedt, R., Vonck, K. E. J., Boon, P., & Holen, R. V. (2014) · Molecular Imaging and Biology · doi:10.1007/s11307-014-0801-9 · Cited by 10
Optimal Information Storage: Nonsequential Sources and Neural Channels
Varshney, L. R. (2006) · Cited by 11
Balanced Recurrent Excitation and Inhibition in Local Cortical Networks
McCormick, D. A., Shu, Y., & Hasenstaub, A. R. (2003) · Springer eBooks · doi:10.1007/978-1-4615-0039-1_8 · Cited by 10
Helmstaedter, M. N., & Feldmeyer, D. (2010) · Springer eBooks · doi:10.1007/978-1-4419-1676-1_8 · Cited by 11
“, summarized in Braitenberg and Schüz (1998) and White (2007)).”
BackgroundGlassman, R. B. (2003) · Brain Research Bulletin · doi:10.1016/s0361-9230(03)00030-3 · Cited by 10
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
