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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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Robinson, P. A., Gao, X., & Han, Y. (2021) · Biological Cybernetics · doi:10.1007/s00422-021-00871-z · Cited by 5
“Here, we show that a simple argument, based on the known approximately exponential decrease of mean connectivity with distance (Braitenberg and Schüz 1998; Henderson and Robinson 2013, 2014; Roberts et al. 2016, 2017), can explain the experimental observation that numbers of axons connecting pairs…”
BackgroundInfluentialBasic Concepts for Spatial Analysis
Freeman, W. J., & Quiroga, R. Q. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4984-3_6 · Cited by 5
Function and Evolution of the Reptilian Cerebral Cortex
Naumann, R. K., & Laurent, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820584-6.00009-x · Cited by 6
Ash, R. T., Palagina, G., Fernández-León, J. A., Park, J., Seilheimer, R. L., Lee, S., Sabharwal, J., Reyes, F., Wang, J., Lu, D., et al. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0654-22.2022 · Cited by 5
Crowe, D. A., Willow, A., Blackman, R. K., DeNicola, A. L., Chafee, M. V., & Amirikian, B. R. (2023) · eLife · doi:10.7554/elife.79352 · Cited by 4
Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders
Grogan, M., Blum, K. P., Wu, Y., Harston, J., Miller, L., & Faisal, A. (2021) · bioRxiv · doi:10.1371/journal.pcbi.1012614 · Cited by 4
“This means the range of the lateral effect parameter σ translates to a 1–2 neuron spacing on our cortical model grid (assuming the radius of dendritic input to these cortical neurons of around 200 μ m [61]).”
Engines of the Brain: The Computational "Instruction Set" of Perception and Cognition.
Granger, R., Petrović, S. A., Felch, A. C., Kerr, J. L., Johnson, M. L., Wuerth, C., & Benvenuto, J. (2004) · Cited by 5
Liley, D. T. J. (2014) · Springer eBooks · doi:10.1007/978-3-642-54593-1_14 · Cited by 6
Costa, L. D. F., & Sporns, O. (2007) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127407018464 · Cited by 5
“omplex networks have already been considered in the context of neuroscience. For example, the nervous system of the nematode C. elegans [3] has been extensively analyzed. Mammalian brain connectivity [4, 5, 6] was found to be characterized by high degrees of clustering, short path lengths, prevalent connectional recip…”
BackgroundWire length as a circuit complexity measure
Legenstein, R. A., & Maass, W. (2004) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2004.06.001 · Cited by 5
“Several empirical studies provide estimates for the order of magnitude of the number n of inputs, the number of neurons, and the length of axons and dendrites (“wires”) in biological neural circuits for sensory processing, see [1,10,18,3] .”
BackgroundEffects of physiological parameter evolution on the dynamics of tonic-clonic seizures
Deeba, F., Sanz‐Leon, P., & Robinson, P. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230510 · Cited by 5
“between populations is proportional to the number of synapses [42,43].”
BackgroundFPGA Implementation of Very Large Associative Memories
Hammerstrom, D. W., Gao, C., Zhu, S., & Butts, M. (2006) · Springer eBooks · doi:10.1007/0-387-28487-7_6 · Cited by 5
“Just as in silicon, connections are expensive to biology, they take space and metabolism, increase the probability of error, and they require genetic material to specify connectivity, Braitenberg and Schuz (1998), Cortex: Statistics and Geometry of Neuronal Connectivity( Braitenberg and Schuz 1998 ): • "metric" (high…”
BackgroundHigh Performance Associative Memory Models with Low Wiring Costs
Calcraft, L., Adams, R., & Davey, N. (2006) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2006.348489 · Cited by 5
“In biological systems, however, such as the mammalian cortex, connectivity is relatively sparse, with each node connecting to only a small subset of other nodes [1].”
BackgroundGao, C. (2008) · PDXScholar (Portland State University) · doi:10.15760/etd.8033 · Cited by 5
Using a genetic algorithm to investigate efficient connectivity in associative memories
Adams, R., Calcraft, L., & Davey, N. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.05.017 · Cited by 6
“These show that in the mouse cortex, for example, there are about 1.6 million neurons, with each connected to, on average, about 8000 other neurons (Braitenberg and Schüz 1998).”
BackgroundInfluentialHow brains are built: Principles of computational neurosciencePreprint
Granger, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.03855 · Cited by 7
Supplementary Material and Methods
Rosato, P. C., Anastasiadou, E., Garg, N., Lenze, D., Boccellato, F., Vincenti, S., Severa, M., Coccia, E. M., Bigi, R., Cirone, M., et al. (2012) · Cited by 3
Local Field Potential, Relationship to BOLD Signal
Logothetis, N. K., & Panzeri, S. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_726-1 · Cited by 5
Robinson, P. A. (2025) · Physical review. E · doi:10.1103/physreve.111.014404 · Cited by 4
Carriere, M., Tomasello, R., & Friedemann Pulvermüller, A. (2025) · Language Cognition and Neuroscience · doi:10.1080/23273798.2025.2596802 · Cited by 5
Biomimetic model of corticostriatal micro-assemblies discovers a neural code
Pathak, A., Brincat, S. L., Organtzidis, H., Strey, H. H., Senneff, S., Antzoulatos, E. G., Mujica-Parodi, L. R., Miller, E. K., & Granger, R. (2025) · Nature Communications · doi:10.1038/s41467-025-67076-x · Cited by 8
Polyakov, D., Robinson, P. A., Müller, E. J., van der Lande, G., Nùñez, P., Annen, J., Gosseries, O., & Shriki, O. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/addd48 · Cited by 4
Coherent noise enables probabilistic sequence replay in spiking neuronal networks
Bouhadjar, Y., Wouters, D. J., Diesmann, M., & Tetzlaff, T. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010989 · Cited by 6
The orbitofrontal cortex, amygdala, reward value, emotion, and decision-making
Rolls, E. T. (2023) · doi:10.1093/oso/9780198887911.003.0011 · Cited by 3
Becker, L. A., Li, B., Priebe, N. J., Seidemann, E., & Taillefumier, T. O. (2024) · Physical Review X · doi:10.1103/physrevx.14.011021 · Cited by 4
“Such variability is striking because neurons are thought to typically receive a large number ( ≃ 10 4 ) of synaptic contacts [13].”
InfluentialRecruiting neural field theory for data augmentation in a motor imagery brain–computer interface
Polyakov, D., Robinson, P. A., Müller, E. J., & Shriki, O. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1362735 · Cited by 4
“To further simplify the model, we assume random intracortical connectivity, leading to N ib = N eb for all b (Braitenberg and Schüz, 1998).”
MethodUnified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity
El-Zghir, R. K., Gabay, N. C., & Robinson, P. A. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1335130 · Cited by 5
“It has been found experimentally that the number of intracortical synapses to different types of cortical neurons is closely proportional to the numbers of neurons involved of each type (Braitenberg and Schüz, 1998), which implies then N ib = N eb for all b in the present case.”
BackgroundSaiki-Ishikawa, A., Agrios, M., Savya, S. P., Forrest, A., Sroussi, H., Hsu, S., Basrai, D., Xu, F., & Miri, A. (2024) · eLife · doi:10.7554/elife.103069.3 · Cited by 22
Structural Models of Cortical Networkswith Long‐Range Connectivity
Voges, N., Aertsen, A., & Rotter, S. (2011) · Mathematical Problems in Engineering · doi:10.1155/2012/484812 · Cited by 4
van Hemmen, J. L., Schüz, A., & Aertsen, A. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0630-6 · Cited by 4
Self-citationTaillefumier, T. O., Touboul, J., & Magnasco, M. O. (2012) · Neural Computation · doi:10.1162/neco_a_00346 · Cited by 4
Wang, K., Riera, J. J., Enjieu-Kadji, H., & Kawashima, R. (2013) · Neural Computation · doi:10.1162/neco_a_00458 · Cited by 5
Neves, K. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00047-6 · Cited by 4
The Slow Depolarization Following Individual Spikes in Thin, Unmyelinated Axons in Mammalian Cortex
Raastad, M. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00203 · Cited by 3
“, 2010) and are usually by far the largest part of neurons’ volume and membrane surface (Braitenberg and Schüz, 1998; Wu et al., 2014).”
BackgroundLee, T., & Tramontano, G. (2021) · World Journal of Neuroscience · doi:10.4236/wjns.2021.112014 · Cited by 3
“Brain statistics show that 1 mm(3) of mouse cortex contains 10(5) neurons, 10(8) synapses, and 4 km of axon [30].”
BackgroundYoo, M., Yang, Y., Rah, J., & Choi, J. H. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1153970 · Cited by 5
“It has N neurons, 80% of which are pyramidal cells and 20% are interneurons (Braitenberg and Schüz, 1998).”
BackgroundNeural field model of seizure-like activity in isolated cortex
Zhao, X., & Robinson, P. A. (2017) · Journal of Computational Neuroscience · doi:10.1007/s10827-017-0642-z · Cited by 3
Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware
Stöckel, A., Ostrau, C., Thies, M., & Rückert, U. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00071 · Cited by 4
“This is a small value compared to the combined input rate of a cortical neuron, which can be roughly estimated to be about 10 4 s −1 (Braitenberg and Schüz, 1998).”
BackgroundThe generation of receptive-field structure in cat primary visual cortex
Martinez, L. M. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)54004-x · Cited by 4
“…the total excitatory synapses made onto cortical cells (LeVay and Gilbert, 1976; Kisvarday et al., 1986; Peters and Payne, 1993; Ahmed et al., 1994; Braitenberg and Schuz, 1998; Binzegger et al., 2004), it is usually assumed that the influence of the feed-forward input on corticalresponse…”
BackgroundInfluentialInfluence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network
Chang, W. (2010) · Communications in Theoretical Physics · doi:10.1088/0253-6102/53/1/40 · Cited by 4
“[4] Several studies have suggested that the topology of connections in real neural networks presents a combination of topological properties such that it can be captured neither by regular connectivity models as lattices, nor by random connections.”
BackgroundEssential circuits of cognition: The brain's basic operations, architecture, and representations
Granger, R. (2006) · Cited by 4
COMPLEXITY IN NEURONAL NETWORKS
Frégnac, Y., Rudolph, M., Davison, A. P., & Alain Destexhe, A. (2007) · Complex systems and interdisciplinary science · doi:10.1142/9789812772367_0009 · Cited by 6
“We have mentioned above the idea that cortical wiring is given by the repetition of canonical microcircuits [53,147,148,149] which was contrasted with the idea that cortical wiring templates are highly variable [150,142,151].”
BackgroundInfluentialBrain-Computer Interfaces Using Brain-Inspired SNN
Kasabov, N. K. (2018) · Springer series on bio- and neurosystems · doi:10.1007/978-3-662-57715-8_14 · Cited by 4
1.2 Physiological Basis of the BOLD Signal
Goense, J. B. M., & Logothetis, N. K. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195372731.003.0002 · Cited by 4
Daffertshofer, A., Frank, T. D., Peper, C. E., & Beek, P. J. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00233256 · Cited by 4
Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortexPreprint
Seeman, S. C., Campagnola, L., Davoudian, P. A., Hoggarth, A., Hage, T. A., Bosma-Moody, A., Baker, C. A., Lee, J. H., Mihalaş, Ş., Teeter, C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/292706 · Cited by 16
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Luboeinski, J. (2021) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-463 · Cited by 4
Dmitrieva, X., Anton, J., Fairs, A., Ivanova, B., Sein, J., Nazarian, B., Dufour, S., Friedemann Pulvermüller, A., Runnqvist, E., & Strijkers, K. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf275 · Cited by 4
“…across production and perception, the spatial dissociations between alveolar and bilabial speech sounds in motor cortex may be communicated to temporal cortex in a topographic manner due to the general neighborhood-preserving nature of cortico-cortical connections (Braitenberg and Schuz 1998).”
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Ascenzi, B. M., Myers, M. W., & Buccilli, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-822404-5.00016-4 · Cited by 5
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Mezzano, S., Uhl, Q., Pavan, T., Nguyen‐Duc, J., Lee, H., Huang, S. Y., & Jelescu, I. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70507 · Cited by 4
“By contrast, histological studies often report higher neurite fractions, up to 60% [34], though this may reflect fixation-induced shrinkage of the extracellular space in ex vivo tissue.”
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Wolf, F. (2005) · Les Houches/Les Houches · doi:10.1016/s0924-8099(05)80018-6 · Cited by 3
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Pękala, D., Baginskas, A., Szkudlarek, H. J., et al. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.280719 · Cited by 3
“…on functions of voltage-sensitive K+ channels (Kvs) because they play an essential role in excitability, transmitter release and bursting, and study PFs because they have dimensions and a transmitter type typical of many cortical grey matter axons (Ramón y Cajal, 1911; Braitenberg & Schüz, 1998).”
BackgroundKapócs, G., Scholkmann, F., Salari, V., Császár, N., Szőke, H., & Bókkon, I. (2016) · Reviews in the Neurosciences · doi:10.1515/revneuro-2016-0047 · Cited by 3
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Berberian, N., Aamir, Z., Hélie, S., & Chartier, S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727875 · Cited by 3
“Such networks formed by intra-cortical connections with strongly interconnected pyramidal cells have attributed to our understanding of auto-associative memory function [8], [9].”
BackgroundAttention and Working Memory in Human Auditory Cortex
Barton, B., & Brewer, A. A. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.85537 · Cited by 3
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“It is based on the modified version of Peter’s rule (21) where (14) assume that synaptic densities on dendrites are independent of the cell types.”
BackgroundWhere Matters: New Approaches to Brain Analysis
Turner, R. W., II (2013) · Springer eBooks · doi:10.1007/978-3-662-45766-5_6 · Cited by 3
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Kehayas, V., & Holtmaat, A. J. D. G. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803784-3.00001-9 · Cited by 3
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Birbaumer, N., & Hummel, F. C. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0595-5 · Cited by 3
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Negrello, M. (2013) · Biological Cybernetics · doi:10.1007/s00422-012-0533-3 · Cited by 3
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Potential connectomics complements the endeavour of ‘no synapse left behind’ in the cortex
Ascoli, G. A. (2012) · The Journal of Physiology · doi:10.1113/jphysiol.2011.225664 · Cited by 3
“It would be too reductive, and often incorrect, to consider synapses simply as a random subset of axo-dendritic proximities or to view axo-dendritic proximities as synaptic predictors (Peter’s rule: Braitenberg and Schüz, 1998).”
BackgroundNeural field theory with variance dynamics
Robinson, P. A. (2012) · Journal of Mathematical Biology · doi:10.1007/s00285-012-0541-x · Cited by 3
“…(i.e., the numbers of synapses made are proportional to the numbers of afferent and efferent neurons available, which is a good approximation (Braitenberg and Schüz 1998; Wright and Liley 1996; Robinson et al. 1997), and that the parameters Q a max , θ a , and σ θ a are the same for all…”
BackgroundTsuda, I., Yamaguti, Y., Kuroda, S., et al. (2008) · Progress of Theoretical Physics Supplement · doi:10.1143/ptps.173.99 · Cited by 3
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Raza, H., Tsegaye, G., & Biswas, R. (2012) · International Journal of Computer Applications · doi:10.5120/5539-7593 · Cited by 3
“The classification problem of XOR is solved.”
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Legéndy, C. R. (2016) · Reviews in the Neurosciences · doi:10.1515/revneuro-2016-0015 · Cited by 3
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Alvandi, E. O. (2015) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-7278-9.ch002 · Cited by 3
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Heinrich, S., Weber, C., Wermter, S., Xie, R., Lin, Y., & Liu, Z. (2016) · neural information processing systems · Cited by 4
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Kasabov, N. K. (2018) · Springer series on bio- and neurosystems · doi:10.1007/978-3-662-57715-8_10 · Cited by 3
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Shahsavari, H., Jokar, H., Haghighi‐Yazdi, M., et al. (2018) · Iranian Journal of Science and Technology Transactions of Mechanical Engineering · doi:10.1007/s40997-018-0267-5 · Cited by 3
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Rauschecker, J. P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-805408-6.00029-4 · Cited by 3
Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States
El-Zghir, R. K., Gabay, N. C., & Robinson, P. A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.642479 · Cited by 3
“Gain parameters can in general be modulated by local feedback Frontiers in Human Neuroscience | www.frontiersin.org 5 June 2021 | Volume 15 | Article 642479 between populations in response to incoming stimuli, which has been useful for analyzing how evoked responses are shaped by attention (Babaie-Janvier and Robinson…”
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Manninen, T., Saudargienė, A., Havela, R., & Linne, M. (2018) · BMC Neuroscience · doi:10.1186/s12868-018-0452-x · Cited by 3
Associative Memories with Small World Connectivity
Davey, N., Calcraft, L., Christianson, B., et al. (2005) · Springer eBooks · doi:10.1007/3-211-27389-1_18 · Cited by 3
“Of course real neural networks have sparse connectivity (for example in the cortex of the mouse each neuron is connected to about 0.1% of the other neurons [ 1 ]), which motivates the investigation undertaken here.”
BackgroundInfluentialZaveri, M. (2008) · PDXScholar (Portland State University) · doi:10.15760/etd.7869 · Cited by 3
Calcraft, L., Adams, R., & Davey, N. (2008) · Biosystems · doi:10.1016/j.biosystems.2008.05.014 · Cited by 3
“It is commonly found that axons travel a certain distance before arborisation [18].”
BackgroundInformation storage capacity of synaptic connectivity patterns
Stepanyants, A., Hof, P. R., & Chklovskii, D. B. (2002) · Neurocomputing · doi:10.1016/s0925-2312(02)00454-x · Cited by 3
Data Mining in Complex Networks: Missing Link Prediction and Fuzzy Communities
Nepusz, T. (2008) · Cited by 3
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Wärnberg, E., & Kumar, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/109900 · Cited by 3
“This approximated the fraction of excitatory and inhibitory neurons found in the primate cortex (Braitenberg and Sch¨uz, 1998).”
BackgroundCosta, L. D. F., & Sporns, O. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.cond-mat/0604089 · Cited by 3
“omplex networks have already been considered in the context of neuroscience. For example, the nervous system of the nematode C. elegans [3] has been extensively analyzed. Mammalian brain connectivity [4, 5, 6] was found to be characterized by high degrees of clustering, short path lengths, prevalent connectional recip…”
BackgroundHeritability of cortical morphology reflects a sensory-fugal plasticity gradientPreprint
Vainik, U., Paquola, C., & Wang, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/dwjr5 · Cited by 3
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Cook, B. J., Peterson, A. D. H., Woldman, W., & Terry, J. R. (2021) · arXiv (Cornell University) · Cited by 3
Cellular dynamics and stable chaos in balanced networks
Touzel, M. P. (2016) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-5477 · Cited by 3
Saiki-Ishikawa, A., Agrios, M., Savya, S. P., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.23.559136 · Cited by 3
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Lehr, A. B., Erzmann, F., Michaelis, C., Nowak, J., Gail, A., Kumar, A., & Tetzlaff, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.25.581959 · Cited by 3
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Eichhorn, J. (2007) · DepositOnce · doi:10.14279/depositonce-1547 · Cited by 3
Coyle, D., Abuhassan, K., & Maguire, L. (2015) · Springer series in computational neuroscience · doi:10.1007/978-3-319-20037-8_9 · Cited by 2
Group Leverage Centrality and its Applications in Brain Networks
Gupta, A., Balaji, H., Sundareswaran, R., Mahesh, V., & Geetanjali, B. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1187/1/012001 · Cited by 2
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Whittingstall, K., & Logothetis, N. K. (2013) · CRC Press eBooks · doi:10.1201/b14756-3 · Cited by 3
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Wei, H., & Li, F. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1254355 · Cited by 2
Garagnani, M. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10061-1 · Cited by 6
“Instead, synaptic projections in the cortex are typically sparse , patchy, and to-pographic (Amir et al. 1993; Braitenberg and Schu¨z 1998; Gilbert and Wiesel 1983, 1989).”
Fueling Brain Inhibition: Integrating GABAergic Neurotransmission and Energy Metabolism
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Bedaux, J., & van Leeuwen, W. A. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2003.12.008 · Cited by 2
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Maham, B. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683456 · Cited by 2
Associative Memory with Small World Connectivity Built on Watts-Strogatz Model
Zhi, X. D., Gao, J., Jing, S. X., & Zhou, Y. (2006) · Lecture notes in computer science · doi:10.1007/11881070_19 · Cited by 2
“Based on the evidence of biology (in the cortex of the mouse each neuron is connected to about 0.1% of the other neurons [ 3 ]), some researchers proposed a Hopfield network with randomly diluted connectivity [5, 6]. Furthermore some purely local connected sparse Hopfield networks have been established, such as modula…”
BackgroundInfluentialSimple Modification of Oja Rule Limits L1-Norm of Weight Vector and Leads to Sparse Connectivity
Aparin, V. (2011) · Neural Computation · doi:10.1162/neco_a_00240 · Cited by 2
“This synaptic pruning results in a sparse connectivity between neurons; only a small fraction of all possible connections between neurons physically exists at both the global and local levels (Markram, Lubke, Frotscher, Roth, & Sakmann, 1997 ; Braitenberg & Schuz, 1998 ; Holmgren, Harkany, Svennenfors, & Zilberter, 20…”
BackgroundON A FRACTIONAL STOCHASTIC PATH INTEGRAL APPROACH IN MODELLING INTERNEURONAL CONNECTIVITY
Bernido, C. C., & Carpio-Bernido, M. V. (2012) · International Journal of Modern Physics Conference Series · doi:10.1142/s2010194512007908 · Cited by 2
Computational neuroimaging: maps and tracks in the human brain
Wandell, B. A., & Dougherty, R. F. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.674141 · Cited by 2
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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