Cortex: Statistics and Geometry of Neuronal Connectivity
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High levels of recurrent connectivity in the source area can lead to labeled axon terminals in the vicinity of retrogradely labeled cells that can be mistaken for long-distance anterograde labeling from the injected target area.
“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 · see citing works
“…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 · see citing works
“connections are local (31), the period of oscil-”
BackgroundInfluentialWhat we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612 · see citing works
Synchronization 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 · see citing works
“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].”
BackgroundWhy 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 · see citing works
“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 · see citing works
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033 · see citing works
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 · see citing works
“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 · see citing works
The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639 · see citing works
“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 · see citing works
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442 · see citing works
“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 1,356 · see citing works
“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 · see citing works
The neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215 · see citing works
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 · see citing works
“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 · see citing works
Pulvermüller, F. (1999) · Behavioral and Brain Sciences · doi:10.1017/s0140525x9900182x · Cited by 1,284 · see citing works
Saturated Reconstruction of a Volume of Neocortex
Kasthuri, N., Hayworth, K. J., Berger, D. R., Schalek, R. L., Conchello, J. A., Knowles-Barley, S., Lee, D., Vázquez-Reina, A., Kaynig, V., Jones, T. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.054 · Cited by 1,044 · see citing works
“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 · see citing works
The high-conductance state of neocortical neurons in vivo
Alain Destexhe, A., Rudolph, M., & Paré, D. (2003) · Nature reviews. Neuroscience · doi:10.1038/nrn1198 · Cited by 1,077 · see citing works
“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 1,043 · see citing works
“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 · see citing works
“…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 · see citing works
Communication in neuronal networks
Laughlin, S. B., & Sejnowski, T. J. (2003) · Science · doi:10.1126/science.1089662 · Cited by 950 · see citing works
“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.”
BackgroundThe neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal
Logothetis, N. K. (2002) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1114 · Cited by 952 · see citing works
Modelling and analysis of local field potentials for studying the function of cortical circuits
Einevoll, G. T., Kayser, C., Logothetis, N. K., & Panzeri, S. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3599 · Cited by 857 · see citing works
Network 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 851 · see citing works
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 888 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
Diversity and Dynamics of Dendritic Signaling
Häusser, M., Spruston, N., & Stuart, G. J. (2000) · Science · doi:10.1126/science.290.5492.739 · Cited by 783 · see citing works
“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
Howarth, C., Gleeson, P., & Attwell, D. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.35 · Cited by 738 · see citing works
“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 739 · see citing works
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 721 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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.”
BackgroundInfluentialHisted, M. H., Bonin, V., & Reid, R. C. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.016 · Cited by 604 · see citing works
“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. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095 · Cited by 638 · see citing works
“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 588 · see citing works
“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 · see citing works
“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 · see citing works
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 490 · see citing works
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 532 · see citing works
“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 520 · see citing works
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 · see citing works
The energetics of CNS white matter
Harris, J. J., & Attwell, D. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3430-11.2012 · Cited by 505 · see citing works
“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 · see citing works
“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 488 · see citing works
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 · see citing works
“, 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 · see citing works
“Based on anatomical considerations (White, 1989; Braitenberg and Schüz, 1997) and neurophysiological measurements (Fetz et al.”
BackgroundResultInfluentialImaging input and output of neocortical networks <i>in vivo</i>
Kerr, J. N. D., Greenberg, D., & Helmchen, F. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506029102 · Cited by 474 · see citing works
Chronic Neural Recording Using Silicon-Substrate Microelectrode Arrays Implanted in Cerebral Cortex
Vetter, R. J., Williams, J. C., Hetke, J. F., Nunamaker, E. A., & Kipke, D. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826680 · Cited by 477 · see citing works
“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 · see citing works
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
Logothetis, N. K., Kayser, C., & Oeltermann, A. (2007) · Neuron · doi:10.1016/j.neuron.2007.07.027 · Cited by 447 · see citing works
“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 443 · see citing works
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 442 · see citing works
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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 445 · see citing works
“(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
Pulvermüller, F., Härle, M., & Hummel, F. C. (2001) · Brain and Language · doi:10.1006/brln.2000.2390 · Cited by 442 · see citing works
Weight consistency specifies regularities of macaque cortical networks
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Energy-Efficient Action Potentials in Hippocampal Mossy Fibers
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The development of gyrification in childhood and adolescence
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Cell assemblies in the cerebral cortex
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“…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 · see citing works
Glutamate metabolism and recycling at the excitatory synapse in health and neurodegeneration
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“Neurodegenerative diseases comprise a variety of disorders of the CNS leading to gradual loss of brain tissue and neuronal death.”
InfluentialLocal 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 · see citing works
Recurrent neuronal circuits in the neocortex
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A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
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Intracortical circuits of pyramidal neurons reflect their long-range axonal targets
Brown, S. P., & Hestrin, S. (2009) · Nature · doi:10.1038/nature07658 · Cited by 357 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
Influence of glutamate and GABA transport on brain excitatory/inhibitory balance
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The fMRI signal, slow cortical potential and consciousness
He, B. J., & Raichle, M. E. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.004 · Cited by 324 · see citing works
“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 · see citing works
“, 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 · see citing works
Self-citationRole of Mechanical Factors in the Morphology of the Primate Cerebral Cortex
Hilgetag, C. C., & Barbas, H. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020022 · Cited by 310 · see citing works
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 304 · see citing works
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 · see citing works
“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 · see citing works
“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 299 · see citing works
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 288 · see citing works
“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 · see citing works
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 · see citing works
“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 284 · see citing works
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 · see citing works
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 283 · see citing works
“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 · see citing works
“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 277 · see citing works
“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 271 · see citing works
“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 · see citing works
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 266 · see citing works
“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 · see citing works
- 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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