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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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Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
“…29 . neurons and brain regions that are spatially close have a relatively high probability of being connected, whereas connections between spatially remote neurons or brain regions are less likely 30–32 . longer axonal projections are more expensive in terms of their material and energy costs 33 .”
BackgroundNeuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
“connections are local (31), the period of oscil-”
BackgroundInfluentialSynchronization in complex networks
Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008) · Physics Reports · doi:10.1016/j.physrep.2008.09.002 · Cited by 3,713
“Indeed, the wiring economy is apparent in the distributions of projection length in neural systems, which show that most neuronal projections are short [196,197].”
BackgroundWhat we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Why do many psychiatric disorders emerge during adolescence?
Paus, T., Keshavan, M., & Giedd, J. N. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2513 · Cited by 3,123
“If the number of synapses per se is unlikely to change the cortical volume and/or thickness, then what other cellular elements could affect it? Approximately 10% of the (mouse) cortex is occupied by glial cells and approximately 60% is occupied by neuropil, which consists of dendritic and axonal processe”
BackgroundInterneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
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Synaptic Energy Use and Supply
Harris, J. J., Jolivet, R., & Attwell, D. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.019 · Cited by 1,747
“Another argument for having a low release probability to reduce energy use depends on the fact that a cortical neuron typically receives about 8,000 synapses on its dendritic tree (Braitenberg and Schüz, 1998).”
Brain mechanisms linking language and action
Pulvermüller, F. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1706 · Cited by 1,828
The importance of mixed selectivity in complex cognitive tasks
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“Random connectivity in small brain regions has been suggested based on anatomical reconstructions [21] and recently observed in the connections from the olfactory bulb to the olfactory cortex [22] (see also [14] for a general discussion).”
BackgroundLogothetis, N. K., & Wandell, B. A. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.66.082602.092845 · Cited by 1,568
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
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“Quantitative anatomical models of individual brain regions suggest that each of the hundreds of thousands or millions of neurons receive many thousands of connections, and each themselves connect to similar numbers of target neurons [16,28].”
BackgroundThe Small World of the Cerebral Cortex
Sporns, O., & Zwi, J. D. (2004) · Neuroinformatics · doi:10.1385/ni:2:2:145 · Cited by 1,378
“Detailed anatomical studies demonstrate an exponential or Gaussian fall-off of connection probability with increasing spatial separation of two neurons (Braitenberg and Schüz, 1998; Hellwig, 2000; Liley and Wright, 1994).”
BackgroundMapping brain maturation and cognitive development during adolescence
Paus, T. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.12.008 · Cited by 1,348
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231
“The Size and Location of Human Visual Cortex Each hemisphere of human cortex spans a surface area on the order of 1000 cm(2) (Van Essen, 2007) and ranges between 2–4 mm in thickness (Blinkov and Glezer, 1968; Braitenberg and Schüz, 1998; Fischl and Dale, 2000).”
Correlated neuronal activity and the flow of neural information
Salinas, E., & Sejnowski, T. J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35086012 · Cited by 1,333
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The neocortical circuit: themes and variations
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Saturated Reconstruction of a Volume of Neocortex
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“This idea underpins theoretical approaches to understanding the brain (Braitenberg and Schuz, 1998; da Costa and Martin, 2013).”
BackgroundThe cost of cortical computation.
Lennie, P. (2003) · Current Biology · doi:10.1016/s0960-9822(03)00135-0 · Cited by 1,134
The high-conductance state of neocortical neurons in vivo
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“A large part of this connectivity originates from the cortex itsel”
BackgroundActive perception: sensorimotor circuits as a cortical basis for language
Friedemann Pulvermüller, A., & Fadiga, L. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2811 · Cited by 1,105
“The multimodal action specificity of mirror neurons suggests action–perception integration at the neuronal level, possibly in the form of neuronal circuits distributed over sensory and motor area”
BackgroundThe Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal
Logothetis, N. K. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-10-03963.2003 · Cited by 1,024
“…cortical neurons may be seen as interneurons, and the traditional cortical scheme of input– elaboration– output, commonly presented as an instantiation of the tripartite perception– cognition–action model, is revealed to be an oversimplification, if not disingenuous (Braitenberg and Schuez, 1998).”
BackgroundThe neural basis of functional brain imaging signals
Attwell, D., & Iadecola, C. (2002) · Trends in Neurosciences · doi:10.1016/s0166-2236(02)02264-6 · Cited by 957
The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal.
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Modelling and analysis of local field potentials for studying the function of cortical circuits
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Communication in neuronal networks
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“Miniaturization, Energy, and Noise The observation that 1 mm3 of mouse cortex contains 105 neurons, 108 synapses, and 4 km of axon (24) suggests that, as in chip design, the brain reduces energy consumption by reducing the size and active area of components.”
BackgroundNetwork anatomy and in vivo physiology of visual cortical neurons
Bock, D. D., Lee, W.-C. A., Kerlin, A., Andermann, M. L., Hood, G., Wetzel, A. W., Yurgenson, S., Soucy, E. R., Kim, H. S., & Reid, R. C. (2011) · Nature · doi:10.1038/nature09802 · Cited by 867
Appraising the brain's energy budget
Raichle, M. E., & Gusnard, D. A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.172399499 · Cited by 925
“This focus would seem reasonable, considering that greater than 80% of neurons are excitatory and greater than 90% of synapses release glutamate (6, 7).”
BackgroundThe excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex
Lefort, S., Tomm, C., Floyd Sarria, J.-C., & Petersen, C. C. H. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.020 · Cited by 875
“Anatomical data demonstrate that the vast majority of synapses in the neocortex are formed between nearby neurons, with long-range axonal projections making smaller contributions (Braitenberg and Schüz, 1998; Douglas et al., 1995).”
BackgroundA Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex
Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M. A., et al. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs270 · Cited by 857
“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 longdistance anterograde labeling from the injected target area.”
BackgroundHeterogeneity in Oscillator Networks: Are Smaller Worlds Easier to Synchronize?
Nishikawa, T., Motter, A. E., Lai, Y., & Hoppensteadt, F. C. (2003) · Physical Review Letters · doi:10.1103/physrevlett.91.014101 · Cited by 846
“Most work has dealt with synchronization of oscillators whose topology of interaction has either the SF or SW [2, 17, 18, 19, 20, 21, 22, 23] property, showing that it leads to improved synchronizability when compared to local lattice topology.”
BackgroundEEG delta oscillations as a correlate of basic homeostatic and motivational processes
Knyazev, G. G. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.10.002 · Cited by 776
Diversity and Dynamics of Dendritic Signaling
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“Generating and maintaining these elaborate structures, which occupy a large proportion of our brains (1), is energetically costly (2), implying that their presence is worth this cost.”
BackgroundUpdated Energy Budgets for Neural Computation in the Neocortex and Cerebellum
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“As described previously (Attwell and Laughlin, 2001), we assume for simplicity that all neurons in the cortex are glutamatergic, as 90% of cells and synapses are glutamatergic (Abeles, 1991; Braitenberg and Schüz, 1998).”
BackgroundNeuron-glia metabolic coupling and plasticity
Magistretti, P. J. (2006) · Experimental Physiology · doi:10.1113/expphysiol.2010.053157 · Cited by 781
Dendritic Excitability and Synaptic Plasticity
Sjöström, P. J., Rancz, E. A., Roth, A., & Häusser, M. (2008) · Physiological Reviews · doi:10.1152/physrev.00016.2007 · Cited by 740
“The vast majority of excitatory synaptic inputs are made onto the dendrites (86), which means that the induction of synaptic plasticity must depend on dendritic properties.”
InfluentialA universal scaling law between gray matter and white matter of cerebral cortex
Zhang, K., & Sejnowski, T. J. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.090504197 · Cited by 723
Cortical high-density counterstream architectures
Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Science · doi:10.1126/science.1238406 · Cited by 616
“Could similar weight-distance relations operate for single neurons, thereby suggesting a similar operational logic over different scales? Like interareal connectivity, local connectivity shows an exponential density decay (10), reflecting the decrease in the likelihood of synaptic contact with distance (111).”
BackgroundWireless Implantable Microsystems: High-Density Electronic Interfaces to the Nervous System
Wise, K. D., Anderson, D. J., Hetke, J. F., Kipke, D. R., & Najafi, K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.820544 · Cited by 656
“To a first approximation, there are approximately 30 000 neurons and 2.4 10 synapses (assuming 8000 synapses/neuron) [80] in a cubic millimeter of the human cortex.”
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“The maximal radius of basal dendrites is typically 100 mm, while axons can extend 500 mm or more (Braitenberg and Schüz, 2001).”
BackgroundKaiser, M., & Hilgetag, C. C. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095 · Cited by 653
“A trend toward wiring minimization is apparent in the distributions of projection lengths for various neural systems, which show that most neuronal projections are short [9–12].”
BackgroundCortical travelling waves: mechanisms and computational principles
Muller, L., Chavane, F., Reynolds, J. H., & B, T. J. S. A. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.20 · Cited by 617
“Connections are mediated by cortical white matter fibres, which can traverse just below the grey matter when connecting two nearby regions or can run in the deep, long-range white matter tracts 60,90 .”
BackgroundCortex and memory: emergence of a new paradigm
Fuster, J. M. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21280 · Cited by 559
“Braitenberg and Schüz (1998) argued that some long fibers (e.g., corticocortical) may actually shorten the effective connectivity within networks.”
BackgroundCortical representations of olfactory input by trans-synaptic tracing
Miyamichi, K., Amat, F., Moussavi, F., Wang, C., Wickersham, I. R., Wall, N. R., Taniguchi, H., Tasic, B., Huang, Z. J., He, Z., et al. (2010) · Nature · doi:10.1038/nature09714 · Cited by 546
Microstructural and functional gradients are increasingly dissociated in transmodal cortices
Paquola, C., de Wael, R. V., Wagstyl, K., Bethlehem, R. A. I., Hong, S., Seidlitz, J., Bullmore, E. T., Evans, A. C., Mišić, B., Margulies, D. S., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000284 · Cited by 500
Synchronization in Networks of Excitatory and Inhibitory Neurons with Sparse, Random Connectivity
Börgers, C., & Kopell, N. (2003) · Neural Computation · doi:10.1162/089976603321192059 · Cited by 516
“Several authors have pointed out that p EI N E and p IE N I are much more important than p EI , N E , p IE , and N I in isolation (Golomb & Hansel, 2000; Tiesinga, Fellous, José, & Sejnowski, 2002; Wang et al., 1995).”
BackgroundInfluentialSensory neural codes using multiplexed temporal scales
Panzeri, S., Brunel, N., Logothetis, N. K., & Kayser, C. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2009.12.001 · Cited by 543
Classification of electrophysiological and morphological neuron types in the mouse visual cortex
Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0417-0 · Cited by 456
The energetics of CNS white matter
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“To calculate synapse density in the rat cerebral cortex (7.36 10 17 synapses/m 3), the number of synapses/neuron [8000: Braitenberg and Schuz (1998)] was multiplied by the density of neurons [9.2 10 13 neurons/m 3: Braitenberg and Schuz (1998)].”
MethodInfluentialMinicolumnar abnormalities in autism
Casanova, M. F., van Kooten, I. A. J., Switala, A. E., van Engeland, H., Heinsen, H., Steinbusch, H. W. M., Hof, P. R., Trippe, J., Stone, J., & Schmitz, C. (2006) · Acta Neuropathologica · doi:10.1007/s00401-006-0085-5 · Cited by 499
“Of the remaining objects, those with areas above the 15th percentile were used for minicolumn fragment detection, while the smaller objects were classified as interneurons based on the fraction of all neurons estimated to be inhibitory by Braitenberg and Schüz [12].”
BackgroundSpike-Phase Coding Boosts and Stabilizes Information Carried by Spatial and Temporal Spike Patterns
Kayser, C., Montemurro, M. A., Logothetis, N. K., & Panzeri, S. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.008 · Cited by 507
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.036 · Cited by 487
“, 2011) and reflects a decrease in the likelihood of synaptic contact with distance (Braitenberg and Schüz, 1998); (2) the lognormal distribution that is observed for interareal weights has also been found for the distribution of synaptic strengths of single neurons (Markov et al.”
Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models
Salinas, E., & Sejnowski, T. J. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-16-06193.2000 · Cited by 472
“Based on anatomical considerations (White, 1989; Braitenberg and Schüz, 1997) and neurophysiological measurements (Fetz et al.”
BackgroundResultInfluentialImaging input and output of neocortical networks in vivo
Kerr, J. N. D., Greenberg, D., & Helmchen, F. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506029102 · Cited by 474
Chronic Neural Recording Using Silicon-Substrate Microelectrode Arrays Implanted in Cerebral Cortex
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“Making a first-order assumption of cell densities in human cortex [ 35 ], this tissue displacement would involve approximately 50 neurons and 40 000 synapses for this type of silicon probe and approximately 1600 neurons and 1 280 000 synapses for this type of microwire array.”
BackgroundSpace–time wiring specificity supports direction selectivity in the retina
EyeWirers, T., Kim, J. S., Greene, M. J., Zlateski, A., Lee, K., Richardson, M., Turaga, S. C., Purcaro, M., Balkam, M., Robinson, A., et al. (2014) · Nature · doi:10.1038/nature13240 · Cited by 450
Toward a quantitative description of large-scale neocortical dynamic function and EEG
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In Vivo Measurement of Cortical Impedance Spectrum in Monkeys: Implications for Signal Propagation
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“47 = 27% partial volume for the extracellular fluid in agreement with that described in the literature (Braitenberg and Schuez, 1998; Peters et al., 1991).”
InfluentialModeling the Spatial Reach of the LFP
Lindén, H., Tetzlaff, T., Potjans, T. C., Pettersen, K. H., Grün, S., Diesmann, M., & Einevoll, G. T. (2011) · Neuron · doi:10.1016/j.neuron.2011.11.006 · Cited by 457
Geometric constraints on human brain function
Pang, J. C. S., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B., Breakspear, M. J., & Fornito, A. (2023) · Nature · doi:10.1038/s41586-023-06098-1 · Cited by 470
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“and that individual neurons are exposed to inputs from large numbers of other neuron”
BackgroundPotjans, T. C., & Diesmann, M. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs358 · Cited by 472
“(2004) use data based on reconstructed neurons that have been filled in vivo with horseradish peroxidase and a modified version of Peters’ rule (Braitenberg and Schüz 1998) based on layer-specific distributions of boutons and dendrites.”
BackgroundMethodResultInfluentialWalking or Talking?: Behavioral and Neurophysiological Correlates of Action Verb Processing
Friedemann Pulvermüller, A., Härle, M., & Hummel, F. C. (2001) · Brain and Language · doi:10.1006/brln.2000.2390 · Cited by 459
Weight consistency specifies regularities of macaque cortical networks
Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386
Energy-Efficient Action Potentials in Hippocampal Mossy Fibers
Alle, H., Roth, A., & Geiger, J. R. P. (2009) · Science · doi:10.1126/science.1174331 · Cited by 429
The development of gyrification in childhood and adolescence
White, T. J. H., Su, S., Schmidt, M., Kao, C., & Sapiro, G. (2009) · Brain and Cognition · doi:10.1016/j.bandc.2009.10.009 · Cited by 433
Cell assemblies in the cerebral cortex
Palm, G., Knoblauch, A., Hauser, F., & Schüz, A. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0596-4 · Cited by 405
Self-citationFriedemann Pulvermüller, A., & Юрьевич, Ш. Ю. (2006) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2006.04.004 · Cited by 414
“…to be realized as discrete strongly connected networks of cortical neurons, sometimes called cell assemblies (Hebb, 1949), neuronal ensembles (Braitenberg and Schüz, 1998; Palm, 1982), distributed functional networks (Roland and Gulyas, 1994), neurocognitive networks (Mesulam, 1998)…”
BackgroundThe glutamate‐glutamine cycle is not stoichiometric: Fates of glutamate in brain
McKenna, M. C. (2007) · Journal of Neuroscience Research · doi:10.1002/jnr.21444 · Cited by 385
Glutamate metabolism and recycling at the excitatory synapse in health and neurodegeneration
Andersen, J. V., Markussen, K. H., Jakobsen, E., Schousboe, A., Waagepetersen, H. S., Rosenberg, P. A., & Aldana, B. I. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108719 · Cited by 392
“Neurodegenerative diseases comprise a variety of disorders of the CNS leading to gradual loss of brain tissue and neuronal death.”
InfluentialRecurrent neuronal circuits in the neocortex
Douglas, R. J., & Martin, K. A. C. (2007) · Current Biology · doi:10.1016/j.cub.2007.04.024 · Cited by 369
Local connectivity and synaptic dynamics in mouse and human neocortex
Campagnola, L., Seeman, S. C., Chartrand, T., Kim, L., Hoggarth, A., Gamlin, C., Ito, S., Trinh, J., Davoudian, P., Radaelli, C., et al. (2022) · Science · doi:10.1126/science.abj5861 · Cited by 363
A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2024) · Science · doi:10.1126/science.adk4858 · Cited by 361
Intracortical circuits of pyramidal neurons reflect their long-range axonal targets
Brown, S. P., & Hestrin, S. (2009) · Nature · doi:10.1038/nature07658 · Cited by 361
Top-down laminar organization of the excitatory network in motor cortex
Weiler, N., Wood, L., Yu, J., Solla, S. A., & Shepherd, G. M. G. (2008) · Nature Neuroscience · doi:10.1038/nn2049 · Cited by 330
Mathematical framework for large-scale brain network modeling in The Virtual Brain
Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.002 · Cited by 356
“This approximation is strictly correct only for the short fibers running within the gray matter (Braitenberg and Schüz, 1998).”
Thalamocortical Circuit Motifs: A General Framework
Halassa, M. M., & Sherman, S. M. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.005 · Cited by 322
Influence of glutamate and GABA transport on brain excitatory/inhibitory balance
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The fMRI signal, slow cortical potential and consciousness
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“Other than activations by specific thalamic inputs described earlier, the superficial layers are also where long-range intracortical and cortico-cortical connections preferentially terminate [31–33].”
BackgroundDense connectomic reconstruction in layer 4 of the somatosensory cortex
Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., & Helmstaedter, M. (2019) · Science · doi:10.1126/science.aay3134 · Cited by 333
“, 2015), and an assumption of random innervation has been put forward 352 (Peters’ rule, (Braitenberg and Schüz, 1998)) and used for massive simulation 353 initiatives (Markram et al.”
BackgroundMethodInfluentialLiewald, D., Miller, R., Logothetis, N., Wagner, H.-J., & Schüz, A. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0626-2 · Cited by 322
Self-citationRole of Mechanical Factors in the Morphology of the Primate Cerebral Cortex
Hilgetag, C. C., & Barbas, H. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020022 · Cited by 311
A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro
Tang, A., Jackson, D., Hobbs, J., Chen, W., Smith, J. L., Patel, H., Prieto, A., Petrusca, D., Grivich, M. I., Sher, A., et al. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3359-07.2008 · Cited by 295
The effects of aerobic activity on brain structure.
Thomas, A. G., Dennis, A., Bandettini, P. A., & Johansen-Berg, H. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00086 · Cited by 303
“Figure 2 provides an approximate breakdown of the components of cerebral gray matter by volume based on several histologic studies (Cherniak, 1990; Syková, 1997; Braitenberg and Schüz, 1998; Chklovskii et al., 2002; Kleim et al., 2007).”
BackgroundMethodInfluentialCrowding: a cortical constraint on object recognition
Pelli, D. G. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.09.008 · Cited by 298
“The density of cortical neurons (per mm(2)) in visual cortex seems to be fixed, stable across individuals and species [57,58], so a fixed number of neurons will occupy a fixed area a.”
BackgroundFunctional Trade-Offs in White Matter Axonal Scaling
Wang, S. S. ‐., Shultz, J. R., Burish, M. J., Harrison, K. H., Hof, P. R., Towns, L. C., WAGERS, M. W., & Wyatt, K. D. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5559-05.2008 · Cited by 304
Neural reuse of action perception circuits for language, concepts and communication
Friedemann Pulvermüller, A. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.07.001 · Cited by 311
“85%) of which are excitatory pyramidal cells, each carrying some 10(4) synapses (Braitenberg and Schüz, 1998).”
BackgroundInfluentialHyder, F., Rothman, D. L., & Bennett, M. R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214912110 · Cited by 271
The Microvascular System of the Striate and Extrastriate Visual Cortex of the Macaque
Weber, B., Keller, A. L., Reichold, J., & Logothetis, N. K. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm259 · Cited by 280
“A typical pyramidal cell in the primate cortex receives approximately 10 000 inputs, and a column underneath 1 mm2 in the visual cortex has approximately 200 000 neurons (Braitenberg and Schuez 1998).”
BackgroundVisual field map clusters in human cortex
Wandell, B. A., Brewer, A. A., & Dougherty, R. F. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1628 · Cited by 296
A connectomic study of a petascale fragment of human cerebral cortex
Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2021) · doi:10.1101/2021.05.29.446289 · Cited by 243
The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions
Neske, G. T. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00088 · Cited by 296
“The vast majority of excitatory synapses onto cortical excitatory neurons come from other cortical excitatory neurons (Braitenburg and Shüz, 1998; Binzegger et al., 2004; Douglas and Martin, 2004).”
BackgroundScalable architecture in mammalian brains
Clark, D. A., Mitra, P. P., & Wang, S. S.-H. (2001) · Nature · doi:10.1038/35075564 · Cited by 276
“Finally, the microscopic composition of neural tissue (neurons, glia, axons and dendrites) varies across mammal”
BackgroundUltrastructural Analysis of Hippocampal Neuropil from the Connectomics Perspective
Mishchenko, Y., Hu, T., Špaček, J., Mendenhall, J. M., Harris, K. M., & Chklovskii, D. B. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.014 · Cited by 276
“Perhaps the oldest method for inferring synaptic connectivity used light microscopy and relies on counting proximities between axons and dendrites that can be bridged by a spine, or so-called potential synapses (Peters and Feldman, 1976; Braitenberg and Schuz, 1998; Stepanyants and Chklovskii, 2005).”
BackgroundMethodInfluentialPlasticity in single neuron and circuit computations
Alain Destexhe, A., & Marder, E. E. (2004) · Nature · doi:10.1038/nature03011 · Cited by 274
“The membrane potential of cortical neurons shows fluctuating activity, mostly of synaptic origin, which is consistent with the extraordinarily dense connectivity in corte”
Neurogeometry and potential synaptic connectivity
STEPANYANTS, A., & CHKLOVSKII, D. (2005) · Trends in Neurosciences · doi:10.1016/j.tins.2005.05.006 · Cited by 254
Towards neural circuit reconstruction with volume electron microscopy techniques
Briggman, K. L., & Denk, W. T. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.08.010 · Cited by 272
“Peters’ rule; [8,9]) are used; attempts have also been made to infer ‘functional connectivity’ from activity correlations in populations [10].”
BackgroundFunctional and structural underpinnings of neuronal assembly formation in learning
Holtmaat, A., & Caroni, P. (2016) · Nature Neuroscience · doi:10.1038/nn.4418 · Cited by 239
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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