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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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Representation of human vision in the brain: how does human perception recognize images?
STARK, L. W., Privitera, C. M., Yang, H., et al. (2001) · Journal of Electronic Imaging · doi:10.1117/1.1329895 · Cited by 43
Neurotransmitter systems in zebrafish model as a target for neurobehavioural studies
Verma, R., Choudhary, P. R., Nirmal, N. K., Syed, F., & Verma, R. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.07.147 · Cited by 44
Generalized cable theory for neurons in complex and heterogeneous media
Bédard, C., & Alain Destexhe, A. (2013) · Physical Review E · doi:10.1103/physreve.88.022709 · Cited by 42
“The small dimension of organelles ( 1 μm2) within cells, as well as the distance between neighboring cells (∼30 nm on average) [28,29] imply that the capacitance values of the media are necessarily small compared to the membrane capacitance, and thus the purely capacitive effects (without diffusion) are likely to be n…”
BackgroundFreeman, W. J., Livi, R., OBINATA, M., et al. (2011) · eScholarship (California Digital Library) · Cited by 42
Networks of habenula-projecting cortical neurons regulate cocaine seeking
Mathis, V., Williams, M., Fillinger, C., & Kenny, P. J. (2021) · Science Advances · doi:10.1126/sciadv.abj2225 · Cited by 38
“3A) ( 56 ).”
Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity
Sadeh, S., Clopath, C., & Rotter, S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004307 · Cited by 35
“Briefly, the model consists of a recurrent network of N = 500 leaky integrate-and-fire (LIF) neurons, of which f = 80% are excitatory and 20% are inhibitory [83].”
MethodSynapse-specific burst coding sustained by local axonal translation
Wong, H. H., Watt, A. J., & Sjöström, P. J. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.011 · Cited by 44
Sleep/wake dependent changes in cortical glucose concentrations
Dash, M. B., Bellesi, M., Tononi, G., & Cirelli, C. (2012) · Journal of Neurochemistry · doi:10.1111/jnc.12063 · Cited by 49
“About 80% of all neurons in the cerebral cortex use glutamate as a neurotransmitter (Braitenberg and Schuz 1998; Jones 2009) and most of the energy in the brain, including the cerebral cortex, is used to support glutamatergic activity (Attwell and Laughlin 2001).”
BackgroundCompensating for synaptic loss in Alzheimer’s disease
Abuhassan, K., Coyle, D., Belatreche, A., & Maguire, L. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0462-8 · Cited by 43
Axonal Noise as a Source of Synaptic Variability
Neishabouri, A., & Faisal, A. A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003615 · Cited by 47
A tissue-level anisotropic criterion for brain injury based on microstructural axonal deformation.
Cloots, R., van Dommelen, J. V., & Geers, M. (2012) · Journal of The Mechanical Behavior of Biomedical Materials · doi:10.1016/j.jmbbm.2011.09.012 · Cited by 44
Kirkcaldie, M. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-369497-3.10004-4 · Cited by 45
Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks
Luboeinski, J., & Tetzlaff, C. (2021) · Communications Biology · doi:10.1038/s42003-021-01778-y · Cited by 41
Cytoarchitecture, areas, and neuron numbers of the Etruscan Shrew cortex
Naumann, R. K., Anjum, F., Roth-Alpermann, C., & Brecht, M. (2012) · The Journal of Comparative Neurology · doi:10.1002/cne.23053 · Cited by 39
“The plane of a coronal section is often close to a true perpendicular section, but this is not accurate in the frontal and occipital poles of the cortex (Braitenberg and Schüz, 1998).”
BackgroundPotjans, W., Morrison, A., & Diesmann, M. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00141 · Cited by 40
Friedemann Pulvermüller, A. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102511 · Cited by 50
“Within each model area, there are sparse and random connections between excitatory neurons whose probability decrease with distance, and each excitatory neuron projects to one inhibitory cell which, in turn, projects back to it and its neighbors, so as to capture some aspects of local cortical connectivity (Braitenber…”
BackgroundLampinen, B., Szczepankiewicz, F., Lätt, J., Knutsson, L., Mårtensson, J., Björkman-Burtscher, I. M., van Westen, D., Sundgren, P. C., Ståhlberg, F., & Nilsson, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120338 · Cited by 38
Complex dynamics in recurrent cortical networks based on spatially realistic connectivities
Voges, N., & Perrinet, L. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00041 · Cited by 40
“Thus, cortical connectivity is neither merely random nor confined to local couplings; in fact, it is a combination of both (Braitenberg and Schüz, 1998; Stepanyants et al., 2009; Voges et al., 2010a,b).”
Enhanced Responsiveness and Low-Level Awareness in Stochastic Network States
Zerlaut, Y., & Alain Destexhe, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.001 · Cited by 38
Mechanical morphogenesis and the development of neocortical organisation
Foubet, O., Trejo, M., & Toro, R. (2015) · Cortex · doi:10.1016/j.cortex.2018.03.005 · Cited by 41
Visible rodent brain-wide networks at single-neuron resolution
Yuan, J., Gong, H., Li, A., Li, X., Chen, S., Zeng, S., & Luo, Q. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2015.00070 · Cited by 37
Müller, E. J., van Albada, S. J., Kim, J. W., & Robinson, P. A. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.06.016 · Cited by 38
“Following Wright and Liley (1996), Robinson et al. (1998) and Braitenberg and Sch¨uz (1998) the intracortical connection strengths are exploited.”
BackgroundCurran, M. M., Sandman, C. A., Davis, E. P., Glynn, L. M., & Baram, T. Z. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180311 · Cited by 39
“These effects of the peptide are important, because neuronal dendritic arbors form the bulk of cortical volume [31,57]; thus, changes in dendrites are likely to be reflected as measurable changes in cortical structure and function.”
BackgroundRobinson, P. A. (2019) · Physical review. E · doi:10.1103/physreve.99.012421 · Cited by 36
Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
Liu, Y. H., Smith, S. J., Mihalaş, Ş., Shea‐Brown, E., & Sümbül, U. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111821118 · Cited by 43
Kreuzer, M., Hentschke, H., Antkowiak, B., Schwarz, C., Kochs, E. F., & Schneider, G. H. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-122 · Cited by 35
Neuronal distribution in the neocortex of schizophrenic patients
Casanova, M. F., Kreczmański, P., Trippe, J., Switala, A. E., Heinsen, H., Steinbusch, H. W. M., & Schmitz, C. (2008) · Psychiatry Research · doi:10.1016/j.psychres.2006.12.009 · Cited by 37
“The latter threshold was based on the fraction of all neurons estimated to be inhibitory by Braitenberg and Schüz (1998). Computerized image analysis produced estimates of the surface fraction (i.”
BackgroundPatterned perturbation of inhibition can reveal the dynamical structure of neural processing
Sadeh, S., & Clopath, C. (2020) · eLife · doi:10.7554/elife.52757 · Cited by 38
Alreja, A., Nemenman, I. M., & Rozell, C. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009642 · Cited by 41
Diffusion spectral imaging modules correlate with EEG LORETA neuroimaging modules
Thatcher, R. W., North, D. M., & Biver, C. J. (2011) · Human Brain Mapping · doi:10.1002/hbm.21271 · Cited by 35
Yu, Y., Karbowski, J., Sachdev, R. N. S., & Feng, J. (2014) · BMC Evolutionary Biology · doi:10.1186/s12862-014-0178-z · Cited by 37
“The lower energy cost of individual neurons could be attributed to the effect of colder temperature on metabolism and the occurrence of fewer synapses in ectothermic brains [55], or the lower energy cost of individual neurons could be associated to the smaller number of glia in ectotherms [46,47,56].”
BackgroundParallels between spacing effects during behavioral and cellular learning
Kornmeier, J., & Sosic-Vasic, Z. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00203 · Cited by 37
“Increasing the spacing interval, i.e., the time between learning repetitions (Figure 1A, Melton, 1970; Toppino and Bloom, 2002; Kahana and Howard, 2005) improves memory performance.”
BackgroundInfluentialFREEMAN, W. J., LIVI, R., OBINATA, M., & VITIELLO, G. (2012) · International Journal of Modern Physics B · doi:10.1142/s021797921250035x · Cited by 47
“Our aim in this report is to summarize our observations of cortical fields in humans and other animals engaged in intelligent behaviors, and then to describe the dynamics of cortex using concepts adapted from non-equilibrium thermodynamics [2] and quantum field theory [3, 4].”
MethodGaragnani, M., Lucchese, G., Tomasello, R., Wennekers, T., & Friedemann Pulvermüller, A. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00145 · Cited by 41
“This produces a sparse, patchy and topographic connectivity, as typically found in the mammalian cortex (Amir et al., 1993; Kaas, 1997; Braitenberg and Schüz, 1998; Douglas and Martin, 2004).”
BackgroundInfluentialAxonal varicosity distributions along parallel fibers: a new angle on a cerebellar circuit
Shepherd, G. M., & Raastad, M. (2003) · The Cerebellum · doi:10.1080/14734220310011407 · Cited by 36
Altwegg‐Boussac, T., Chávez, M., Mahon, S., & Charpier, S. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2013.270561 · Cited by 34
“This ongoing activity, which mainly originates from profuse intracortical synaptic connections (Braitenberg & Schüz, 1998; Destexhe et al. 2003), varies widely in amplitude and frequency as a function of the vigilance state and anaesthetic regime.”
BackgroundPropagating synchrony in feed-forward networks
Jahnke, S., Memmesheimer, R.-M., & Timme, M. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00153 · Cited by 34
“Since cortical neural networks are overall sparse (e.g., Braitenberg and Schüz, 1998; Holmgren et al., 2003), we may also expect some level of dilution for embedded feed-forward chains.”
BackgroundAn Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks
Cabessa, J., & Villa, A. E. P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094204 · Cited by 35
“Certain pathways through the network may be favoured by preferred synaptic interactions between the neurons following developmental and learning processes [47–49].”
BackgroundNeuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System
Mikaitis, M., García, G. P., Knight, J. C., & Furber, S. B. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00105 · Cited by 35
“However, over recent years, it has been found that, on average, cortical neurons have 8,000 input synapses (Beaulieu and Colonnier, 1989; Pakkenberg et al., 2003; Braitenberg and Schüz, 2013) each receiving spikes at the lower average rate of around 3 Hz (Buzsaki and Mizuseki, 2014).”
BackgroundRepresenting Where along with What Information in a Model of a Cortical Patch
Roudi, Y., & Treves, A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000012 · Cited by 36
“Thus, it would seem rather implausible that the brain uses its storage capacity, arguably its most precious resource [29], to store something that it does not have to retrieve.”
BackgroundVincent, M., Rossel, O., Hayashibe, M., Herbet, G., Duffau, H., Guiraud, D., & Bonnetblanc, F. (2015) · Reviews in the Neurosciences · doi:10.1515/revneuro-2015-0029 · Cited by 38
“Braitenberg and Schüz (1998) observed that the maximum radius of basal dendrites is 100 μm, while axons can extend by 500 μm or more.”
BackgroundGelens, F., Äijälä, J., Roberts, L., Komatsu, M., Uran, C., Jensen, M. A., Miller, K. J., Ince, R. A. A., Garagnani, M., Vinck, M. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48329-7 · Cited by 35
“…and area-specific global regulation mechanisms (referred to as local and global inhibition, respectively) (Yuille and Geiger, 1998; Palm et al., 2014; Braitenberg and Sch¨uz, 1998); 5) Single cells’ neurophysiological dynamics, including sigmoid transformation of membrane potentials into neuronal…”
Scale-Free Cortical Planar Networks
Freeman, W. J., Kozma, R., Bollobá, B., & Riordan, O. M. (2008) · Bolyai Society mathematical studies · doi:10.1007/978-3-540-69395-6_7 · Cited by 36
The Genetic Analysis of Functional Connectomics in Drosophila
Meinertzhagen, I. A., & Lee, C. (2012) · Advances in genetics · doi:10.1016/b978-0-12-404742-6.00003-x · Cited by 36
Beul, S. F., Goulas, A., & Hilgetag, C. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006550 · Cited by 31
“…derived from these observations were that axons grow randomly across the cortical sheet (i.e., with no particular spatial orientation) and that they indiscriminately form connections once they are close enough to a potential target neuron, a mechanism that has been named Peters’s Rule [82, 83].”
BackgroundDoborjeh, M., Doborjeh, Z., Merkin, A., Bahrami, H., Sumich, A. L., Krishnamurthi, R., Medvedev, O. N., Crook‐Rumsey, M., Morgan, C., Kirk, I. J., et al. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.013 · Cited by 36
Glutamate and Glutamate Receptors
Hassel, B., & Dingledine, R. J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-374947-5.00017-1 · Cited by 34
Self-organization and the selection of pinwheel density in visual cortical development
Kaschube, M., Schnabel, M., & Wolf, F. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/1/015009 · Cited by 35
Dynamic stability of sequential stimulus representations in adapting neuronal networks
Duarte, R., & Morrison, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00124 · Cited by 33
“Given the ubiquity of inhibition in the cortex (∼20% of all cortical neurons are inhibitory, see Braitenberg and Schüz, 1998) and its undeniable role in shaping and stabilizing network dynamics and neuronal excitability, the possible functional implications of dynamic inhibition are of great…”
BackgroundMethodInfluentialRopireddy, D., Bachus, S. E., & Ascoli, G. A. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2011.12.055 · Cited by 35
“Because of tight somatic packing, the principal layer volume directly affects the absolute numbers of principal cells (Boss et al., 1987; Amaral et al., 1990; Braitenberg and Schultz, 1998).”
BackgroundLocal neuronal excitation and global inhibition during epileptic fast ripples in humans
Curot, J., Barbeau, E. J., Despouy, E., Denuelle, M., Sol, J. C., Lotterie, J.-A., Valton, L., & Peyrache, A. (2022) · Brain · doi:10.1093/brain/awac319 · Cited by 35
Yaşar, T. B., Gombkötő, P., Vyssotski, A. L., Vavladeli, A., Lewis, C. M., Wu, B., Meienberg, L., Lundegardh, V., Helmchen, F., von der Behrens, W., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49226-9 · Cited by 37
“This is one order of magnitude smaller than the cross-sectional area of the soma of a typical neuron 25 .”
Yang, D., & Robinson, P. A. (2017) · Physical review. E · doi:10.1103/physreve.95.042410 · Cited by 33
Neuronal contact predicts connectivity in the C. elegans brain
Cook, S. J., Kalinski, C. A., & Hobert, O. (2023) · Current Biology · doi:10.1016/j.cub.2023.04.071 · Cited by 30
Bujan, A. F., Aertsen, A., & Kumar, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4536-14.2015 · Cited by 36
“It has been argued that the connectivity of cortical microcircuits can be approximated by a random model (Braitenberg and Schüz, 1998), but recent anatomical and physiological studies indicate that at least connection probability decreases with distance (for review, see Boucsein et al., 2011),…”
Structural Plasticity of Circuits in Cortical Neuropil
Escobar, G. M., Fares, T., & Stepanyants, A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2046-08.2008 · Cited by 33
“As reported in the studies by Schüz and Palm (1989) and Braitenberg and Schüz (1998), this density is 0.91 m 3 (n 3 mice).”
BackgroundMethodResultInfluentialHow astrocytic ATP shapes neuronal activity and brain circuits
Lezmy, J. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102685 · Cited by 35
Casanova, M. F., Trippe, J., Tillquist, C. R., & Switala, A. E. (2009) · Journal of Anatomy · doi:10.1111/j.1469-7580.2008.01027.x · Cited by 32
“Of the remaining objects, those with areas above the 15th percentile were used for minicolumn fragment detection, and the smaller objects were classified as interneurons, based upon the fraction of all neurons estimated to be inhibitory by Braitenberg & Schüz (1998).”
MethodChronic loss of inhibition in piriform cortex following brief, daily optogenetic stimulation
Ryu, B., Nagappan, S., Santos-Valencia, F., Lee, P., Rodriguez, E., Lackie, M., Takatoh, J., & Franks, K. M. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109001 · Cited by 36
“Cortical neurons communicate via recurrent excitatory synaptic connections, with each cell typically receiving excitatory inputs from thousands of other cortical neurons (Braitenberg and Schüz, 1998; Douglas et al., 1995).”
Scaling the Brain and Its Connections
Changizi, M. (2007) · Evolution of Nervous Systems · doi:10.1016/b0-12-370878-8/00061-6 · Cited by 32
Functional imaging of the visual pathways
Wandell, B. A., & Wade, A. R. (2003) · Neurologic Clinics · doi:10.1016/s0733-8619(03)00003-3 · Cited by 32
“Eighty-five percent of cortical neurons are pyramidal cells, and these are the cells that dominate these field potentials at the scale [45].”
BackgroundZhang, X., Foderaro, G., Henriquez, C. S., & Ferrari, S. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065717500150 · Cited by 31
Auditory Cortical Local Subnetworks Are Characterized by Sharply Synchronous Activity
Atencio, C. A., & Schreiner, C. E. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2014-13.2013 · Cited by 31
“This compares favorably to estimates of connection probability from anatomical considerations (0.864; Braitenberg and Schüz, 1998).”
ResultInfluentialHigh capacity, small world associative memory models
Davey, N., Calcraft, L., & Adams, R. (2006) · Connection Science · doi:10.1080/09540090600639339 · Cited by 31
“At the level of individual neurons the connectivity pattern is so complex that only generalised statistics can be produced, see ( Braitenberg, and Schuz, 1998 ) for a fascinating discussion.”
BackgroundMicroscale Inhomogeneity of Brain Tissue Distorts Electrical Signal Propagation
Nelson, M. J., Bosch‐Bouju, C., Venance, L., & Pouget, P. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3502-12.2013 · Cited by 35
“Indeed extracellular fluid is thought to comprise only 12–25% of the brain’s volume (Peters et al., 1991; Braitenberg and Schuez, 1998).”
BackgroundBirdsey-Benson, A., Gill, A., Henderson, L., & Madden, D. R. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4558-09.2010 · Cited by 33
“Glutamate is the most common neurotransmitter in the brain, mediating signals at 80% of the synapses in the cortex (Braitenberg and Schüz, 1998).”
BackgroundCortico-cortical communication dynamics
Roland, P., Hilgetag, C., & Deco, G. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00019 · Cited by 31
“With an estimated average convergence and divergence rate of cortical neurons in the order of 104– 105 inputs and outputs (Braitenberg and Schüz, 1998), realistic models even of small cortical patches require the inclusion of several 10,000s of neurons (Potjans and Diesmann, 2012).”
BackgroundMüller, E. J., & Robinson, P. A. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00098 · Cited by 30
“Following the approach of previous neural field models, excitatory and inhibitory synapses in the cortex are assumed proportional to the numbers of neurons (Wright and Liley, 1996; Braitenberg and Schüz, 1998).”
MethodSpectral Characterization of Hierarchical Network Modularity and Limits of Modularity Detection
Sarkar, S., Henderson, J. A., & Robinson, P. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054383 · Cited by 28
Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise
Droste, F., & Lindner, B. (2017) · Journal of Computational Neuroscience · doi:10.1007/s10827-017-0649-5 · Cited by 30
Fluxonic Processing of Photonic Synapse Events
Shainline, J. M. (2019) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2927473 · Cited by 27
Fujita, S., Buonincontri, G., Cencini, M., Fukunaga, I., Takei, N., Schulte, R. F., Hagiwara, A., Uchida, W., Hori, M., Kamagata, K., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25232 · Cited by 28
“The contrast due to high degree of myelination, well known in the primary visual areas (Braitenberg & Schüz, 1998), has decreased the gray/white matter contrast resulting in missegmentation in FSPGR.”
BackgroundBédard, C., & Alain Destexhe, A. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511979958.005 · Cited by 32
A Quantitative Theory of Neural Computation
Valiant, L. G. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0079-3 · Cited by 28
“6*10(7) and d = 7800 have been given for the mouse (Braitenberg and Schuz, 1998) and of n = 10(10) and d = 2.”
BackgroundInfluentialSpike Correlations – What Can They Tell About Synchrony?
Tchumatchenko, T., Geisel, T., Volgushev, M., & Wolf, F. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00068 · Cited by 32
“Each cortical neuron receives inputs from approximately 10 4 other neurons and sends out signals via its synapses to about 10 4 others (Abeles, 1991; Braitenberg and Schüz, 1998).”
BackgroundMárquez, J., Matés, J. M., & Campos-Sandoval, J. A. (2016) · Advances in neurobiology · doi:10.1007/978-3-319-45096-4_6 · Cited by 32
The apical dendrite theory of consciousness
LaBerge, D., & Kasevich, R. S. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.09.006 · Cited by 32
How Retinal Ganglion Cells Prevent Synaptic Noise From Reaching the Spike Output
Demb, J. B., Sterling, P., & Freed, M. A. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00108.2004 · Cited by 28
“They are comparable in size, with thickish, longish dendrites and Ն1,000 excitatory inputs (Braitenberg and Schüz 1998; Freed and Sterling 1988; Kier et al. 1995; Shadlen and Newsome 1994, 1998); and both have a low maintained discharge (ϳ5 spikes/s) so that spike responses resemble " bursts.”
BackgroundChronaxie Measurements in Patterned Neuronal Cultures from Rat Hippocampus
Stern, S., Agudelo-Toro, A., Rotem, A., Moses, E., & Neef, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132577 · Cited by 28
“4 km of cable compared to 4 km of axons in a typical 1 mm(3) cube of cortex [12], dendrites are important information processors and a relevant target for stimulation.”
BackgroundComputational capacity of pyramidal neurons in the cerebral cortex
Георгиев, Д., Kolev, S. K., Cohen, E., & Glazebrook, J. F. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147069 · Cited by 30
“Axons of cortical pyramidal neurons form between 7000 and 8000 synapses onto target neurons (Braitenberg & Schüz, 1998).”
BackgroundPhase synchronization and measure of criticality in a network of neural mass models
Kazemi, S., & Jamali, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-05285-w · Cited by 26
Mishchenko, Y. (2010) · Nature Precedings · doi:10.1371/journal.pone.0008853 · Cited by 28
“Although our study was performed using a sample from rodent neuropil, its results primarily depend on two parameters: the mean density of synapses in neuropil, ,1–2 mm(23), and the typical size of synapses, ,300 nm [32,33,34,35,36,38].”
BackgroundMethodUniversality in visual cortical pattern formation
Wolf, F., & Geisel, T. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2003.09.018 · Cited by 28
“In every mm3 of cortical tissue in the order of 106 neurons must be wired appropriately for their respective functions such as the analysis of sensory inputs, the storage of skills and memory, or motor control [ 11 ].”
BackgroundMethodInfluentialSong, Y., Makarov, V. A., & Velárde, M. G. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0326-5 · Cited by 28
“As amatter of fact, the brain organization can be viewed in gross sense as a number of local subnetworks (graymatter) coupled by long distance connections (whitematter) (Braitenberg and Schuz 1998; Kandel et al. 2000).”
BackgroundNeural Metabolic Networks: Key Elements of Healthy Brain Function
Madrer, N., Perera, N. D., Uccelli, N. A., Abbondanza, A., Andersen, J. V., Carsana, E. V., Demmings, M. D., Fernandez, R. F., de Fragas, M. G., Gbadamosi, I., et al. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70084 · Cited by 37
“Early studies indicate that around 90% of the synapses in the CNS are glutamatergic (Abeles 1991; Braitenberg and Schüz 1998), whose activity consumes roughly 80% of the whole brain's energy (Attwell and Laughlin 2001; Howarth et al. 2012).”
Approximate Invariance of Metabolic Energy per Synapse during Development in Mammalian Brains
Karbowski, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033425 · Cited by 28
“The numerical values of these parameters are: VNa~0:050 V, VK~{0:100 V, V0~{0:065 V (standard values), (1{w)&0:65 [26,27,28,56], gNa,0~3:10 {7 (Vcm(2)) [33], C&3:2:10{6 F/cm(2), and d~0:45:10{4 cm [33].”
BackgroundMethodInfluentialReceptive Field Self-Organization in a Model of the Fine Structure in V1 Cortical Columns
Lücke, J. (2009) · Neural Computation · doi:10.1162/neco.2009.07-07-584 · Cited by 27
“As not much is known about a structured connectivity within the subpopulations of layer 2/3, we assume random connectivity (compare Braitenberg & Schüz, 1998; Maass, Natschläger, & Markram, 2002; Kalisman, Silberberg, & Markram, 2005; Amirikian, 2005; Muresan & Savin, 2007).”
BackgroundSpecificity and randomness: structure–function relationships in neural circuits
Lee, W.-C. A., & Reid, R. C. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.07.004 · Cited by 29
“Although some have argued that the local cortical network is for the most part randomly connected, other than topographic specificity [1], detailed examination of cortical connectivity supports the idea that synaptic connectivity is specific at several different levels.”
BackgroundEnergy supply per neuron is constrained by capillary density in the mouse brain
Ventura‐Antunes, L., & Herculano‐Houzel, S. (2022) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.760887 · Cited by 26
Enhanced behavioral performance through interareal gamma and beta synchronization
Parto-Dezfouli, M., Vezoli, J., Bosman, C. A., & Fries, P. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113249 · Cited by 29
Stability switches, bifurcation, and multi-stability of coupled networks with time delays
Mao, X. (2011) · Applied Mathematics and Computation · doi:10.1016/j.amc.2011.11.096 · Cited by 27
Compensating for thalamocortical synaptic loss in Alzheimer's disease
Abuhassan, K., Coyle, D., & Maguire, L. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00065 · Cited by 27
“It has been observed that there is 350,000 thalamocortical connections per 11,000,000 neurons in layer 4 of the primary visual cortex (Binzegger et al., 2004) leading to a ratio of 1/30 (Izhikevich and Edelman, 2008).”
BackgroundYu, Q., Reutens, D. C., O’Brien, K., & Vegh, V. (2016) · Human Brain Mapping · doi:10.1002/hbm.23441 · Cited by 26
How chaotic is the balanced state?
Jahnke, S. (2009) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.013.2009 · Cited by 27
Nikolić, D. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107820 · Cited by 27
A 3D atlas of functional human brain energetic connectome based on neuropil distribution
Yu, Y., Akif, A., Hermán, P., Cao, M., Rothman, D. L., Carson, R. E., Agarwal, D., Evans, A. C., & Hyder, F. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac322 · Cited by 27
Modeling of the Neurovascular Coupling in Epileptic Discharges
Voges, N., Blanchard, S., Wendling, F., Olivier David, A., Benali, H., Papadopoulo, T., Clerc, M., & Bénar, C. (2011) · Brain Topography · doi:10.1007/s10548-011-0190-1 · Cited by 26
“Their effect can be comparably strong because several inh. cells preferably connect to proximal parts of their exc. targets (Braitenberg and Schüz 1998).”
Jelescu, I., Ciobanu, L., Geffroy, F., Marquet, P. P., & Bihan, D. L. (2014) · NMR in Biomedicine · doi:10.1002/nbm.3061 · Cited by 28
“As expected, this value is already much larger than in mammalian nervous tissue (15%) (33), while for Aplysia it is a lower limit, since smaller cell bodies were not accounted for.”
BackgroundReconstructing Cortical Networks: Case of Directed Graphs with High Level of Reciprocity
Nepusz, T., Négyessy, L., Tusnády, G., & Bazsó, F. (2008) · Bolyai Society mathematical studies · doi:10.1007/978-3-540-69395-6_8 · Cited by 24
“The cortex, in general (ignoring species and areal density differences [16]) consists of approximately 10(10) nerve cells, each receiving numerous connections of order 10(3) (up to about 10(4)) [7].”
BackgroundQuantitative analysis of functional clustering of neurons in the macaque inferior temporal cortex
Tamura, H., Kaneko, H., & Fujita, I. (2005) · Neuroscience Research · doi:10.1016/j.neures.2005.04.006 · Cited by 26
“The probability of having synaptic contacts is much higher between nearby neurons than between distant neurons (Douglas et al., 1995; Braitenberg and Schüz, 1998).”
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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