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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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Neuronal circuits sustaining neocortical-injury-induced status epilepticus
Singh, T., Batabyal, T., & Kapur, J. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105633 · Cited by 25
“Braintenberg and Schuz found that only 1 in 100 or even 1000 white matter fibers connect the cortex with subcortical areas; inter-and intra- hemispheric cortical projections constitute the rest of the white matter fibers (Braitenberg et al., 1998).”
BackgroundBrain-Language Research: Where is the Progress?
Friedemann Pulvermüller, A. (2010) · Biolinguistics · doi:10.5964/bioling.8791 · Cited by 27
Generalized seizures in a neural field model with bursting dynamics
Zhao, X., & Robinson, P. A. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0571-7 · Cited by 25
“In (13–15) we have assumed the approximation of cortical connectivity (Braitenberg and Schüz 1998; Robinson et al. 1998; Wright and Liley 1996) where the number of projections from each source neuron to each target population is proportional the target population’s size.”
MethodLarge-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
Knight, J. C., Tully, P. J., Kaplan, B. A., Lansner, A., & Furber, S. B. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00037 · Cited by 25
“This number is consistent with averages calculated across cortical layers and areas in mice (Braitenberg and Schüz, 2013), cats (Beaulieu and Colonnier, 1989) and humans (Pakkenberg et al., 2003).”
MethodResultInfluentialA subpopulation of cortical VIP-expressing interneurons with highly dynamic spines
Georgiou, C., Kehayas, V., Lee, K. S., Brandalise, F., Sahlender, D. A., Blanc, J., Knott, G. W., & Holtmaat, A. J. D. G. (2022) · Communications Biology · doi:10.1038/s42003-022-03278-z · Cited by 26
A Phenomenological Theory of Spatially Structured Local Synaptic Connectivity
Amirikian, B. R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010011 · Cited by 24
Slow and fast pulses in 1-D cultures of excitatory neurons
Álvarez-Lacalle, E., & Moses, E. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0123-5 · Cited by 25
“We will consider connectivities of 10%, 20% and 30% (α = 0.1, 0.2 and 0.3) in agreement with typical values in the brain (Braitenberg and Schüz 1998).”
BackgroundResultInfluentialCoordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis
Reichl, L., Heide, D., Löwel, S., Crowley, J. C., Kaschube, M., & Wolf, F. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002466 · Cited by 24
“Some symmetries are widely considered biologically plausible for cortical maps, for instance the invariance under spatial translations and rotations or a global shift of orientation preference [51,63,67–71].”
BackgroundDetermination of Dynamic Brain Connectivity via Spectral Analysis
Robinson, P. A., Henderson, J. A., Gabay, N. C., Aquino, K. M., Babaie-Janvier, T., & Gao, X. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.655576 · Cited by 23
“…matches that seen in the brain (Nunez, 1995; Robinson et al., 1997, 2016; Nunez et al., 2001; Mukta et al., 2019, 2020) and yields eigenfunctions that are very similar to those obtained from connectivity matrices and anatomical studies (Robinson et al., 1997, 2016; Braitenberg and Schüz, 1998).”
BackgroundResultneuroMAP — Interactive graph-visualization of the fruit fly's neural circuit
Sorger, J., Bühler, K., Schulze, F., Liu, T., & Dickson, B. J. (2013) · IEEE Symposium on Biological Data Visualization (BioVis) · doi:10.1109/biovis.2013.6664349 · Cited by 27
“The relationship between the connection of two neurons and the overlap of their arborizations can be described by Peters’ rule [7].”
BackgroundStructural Brain Network: What is the Effect of LiFE Optimization of Whole Brain Tractography?
Qi, S., Meesters, S., Nicolay, K., Romeny, B. M. T. H., & Ossenblok, P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00012 · Cited by 22
“It is in accordance with anatomical histology which shows even more short fibers ( < 10 mm; Braitenberg and Schuz, 1998) and neural development mechanism promoting short wiring (Sporns, 2010).”
Digital VLSI for neural networks
Hammerstrom, D. W. (1998) · Cited by 24
Local Field Potentials, BOLD, and Spiking Activity: Relationships and Physiological MechanismsPreprint
Philipp, B., Logothetis, N., & Τόλιας, Α. (2010) · Nature Precedings · doi:10.1038/npre.2010.5216.1 · Cited by 26
“Every cortical pyramidal cell receives approximately 10,000 synaptic inputs, about 75% of which are excitatory, the exact numbers varying between structures and species (Abeles, 1991; Braitenberg and Schüz, 1998).”
BackgroundDendritic plateau potentials can process spike sequences across multiple time-scales
Leugering, J., Nieters, P., & Pipa, G. (2023) · Frontiers in Cognition · doi:10.3389/fcogn.2023.1044216 · Cited by 24
“However, 22 computational properties of neural systems are also dependent on the structure and dynamics in the neural dendrites of 23 single neurons [13], which make up the vast majority of neural tissue [14].”
BackgroundBrain-constrained neural modeling explains fast mapping of words to meaning
Constant, M., Friedemann Pulvermüller, A., & Tomasello, R. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad007 · Cited by 27
“The probability of a synapse to be created between any two cells falls off with their distance (Braitenberg and Schüz 1998) according to a Gaussian function clipped to 0 outside the chosen neighborhood (a square of size n = 19 for excitatory and n = 5 for inhibitory cell projections).”
BackgroundResultInfluentialInferring Network Dynamics and Neuron Properties from Population Recordings
Linaro, D., Storace, M., & Mattia, M. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00043 · Cited by 24
“…neurons of the same pool. might be thought of a rather rough hypothesis has been proved to be a direct consequence of the central limit theorem (Amit and Tsodyks, 1991), which well applies to the large number of connections on the dendritic trees of cortical neurons (Braitenberg and Schüz, 1998).”
BackgroundConcentric zones for pheromone components in the mushroom body calyx of the moth brain
Namiki, S., Takaguchi, M., Seki, Y., Kazawa, T., Fukushima, R., Iwatsuki, C., & Kanzaki, R. (2012) · The Journal of Comparative Neurology · doi:10.1002/cne.23219 · Cited by 23
“The overlap of dendrites and axons gives an estimate of synaptic connectivity; this is called Peters’s rule (Peters and Feldman, 1976; Braitenberg and Schüz, 1998).”
BackgroundPhysiologically based calculation of steady-state evoked potentials and cortical wave velocities
Robinson, P. A., Chen, P., & Yang, L. (2007) · Biological Cybernetics · doi:10.1007/s00422-007-0191-z · Cited by 23
“If intracortical connectivities are proportional to the numbers of synapses involved (Braitenberg and Schüz 1998), one finds Vi = Ve and Qi = Qe (Robinson et al. 1997; Wright and Liley 1996), which lets us concentrate on excitatory quantities (inhibitory effects are retained and can be calculated…”
BackgroundA model of feedback control for the charge-balanced suppression of epileptic seizures
Lopour, B. A., & Szeri, A. J. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0215-x · Cited by 23
“Roughly 90 percent of all cortical synapses are excitatory and 10 percent are inhibitory (Abeles 1991; Braitenberg and Schüz 1998).”
Garnier, A., Vidal, A. C., & Benali, H. (2016) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-016-0042-0 · Cited by 22
“Following [34], each Ci is proportional to the maximum number C of synapses between two populations.”
BackgroundThe place of ‘codes’ in nonlinear neurodynamics
Freeman, W. J. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65028-0 · Cited by 22
“Yet it is ceaselessly active at all times in responding to input from an average 10,000 other neurons (Braitenberg and Schüz, 1998) by which it is modulated through multiple order parameters.”
BackgroundWeiß, D., Breit, S., Hoppe, J., Hauser, A., Freudenstein, D., Krüger, R., Sauseng, P., Govindan, R. B., & Gerloff, C. P. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08014.x · Cited by 22
“Neuronal synchronization was shown to strengthen synaptic transmission (Braitenberg & Schüz, 1998) and, in line with this, efficacy of motor integration depended on the strength of neuronal synchronization in remote functional neuronal networks (von Stein & Sarnthein, 2000; Hummel & Gerloff, 2006).”
BackgroundHow does the speed of thought compare for brains and digital computers?
Nagarajan, N., & Stevens, C. F. (2008) · Current Biology · doi:10.1016/j.cub.2008.06.043 · Cited by 23
Extracting synaptic conductances from single membrane potential traces
Pospischil, M., Piwkowska, Z., Bal, T., et al. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.10.033 · Cited by 23
“Each pyramidal neuron receives btween 5000 and 60000 synaptic contacts and a large part of this connectivity originates from the cortex itself (DeFelipe and Farinas, 1992; Braitenberg and Schüz, 1998).”
Fixing the location and dimensions of functional neocortical columns
Markram, H. (2008) · HFSP Journal · doi:10.2976/1.2919545 · Cited by 22
“Different models have been developed to systematically study structural connectivity (Uttley, 1956; Liley and Wright, 1994; Braitenberg and Schuz, 1998; Stepanyants et al., 2002; Kalisman et al., 2003).”
BackgroundVisually Driven Neuropil Activity and Information Encoding in Mouse Primary Visual Cortex
Lee, S., Meyer, J. F., Park, J., & Smirnakis, S. M. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00050 · Cited by 24
“One possible explanation for this difference is that a local volume of neuropil integrates numerous synaptic processes (Braitenberg and Shchuz, 1998; Chklovskii et al., 2002) that may extend over considerable cortical distances, providing the same signal to different neuropil patches.”
BackgroundResultInfluentialKwon, O., Feng, L., Druckmann, S., & Kim, J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0155-18.2018 · Cited by 28
“Peters’ rule, a method proposed to predict synaptic connectivity on the basis of geometric overlaps between axon and dendrite, has been interpreted to provide support for random synaptic connectivity (Peters and Feldman, 1976; Braitenberg and Schüz, 1998).”
MethodMajor Feedforward Thalamic Input Into Layer 4C of Primary Visual Cortex in Primate
García‐Marín, V., Kelly, J. G., & Hawken, M. J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx311 · Cited by 22
“…onto cortical neurons as these numbers are part of the puzzle in determining the strength of synaptic integration from different sources (feedforward and feedback) and hence functional spiking properties of the different neuronal populations (Braitenberg and Schüz 1998; Oberlaender et al. 2012).”
BackgroundMethodInfluentialPrinciples of visual cortex excitatory microcircuit organization
Chou, C. Y. C., Wong, H. H., Guo, C., Boukoulou, K. E., Huang, C., Jannat, J., Klimenko, T., Li, V. Y., Liang, T. A., Wu, V. C., et al. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100735 · Cited by 26
“For PCs, we reproduced the V1 canonical circuit, 4-6,53 but we found surprising differences for BCs and MCs. Finally, E / E pathways were weaker than E / I pathways.”
Knoblauch, K., Ercsey-Ravasz, M., Kennedy, H., & Toroczkai, Z. (2016) · Research and perspectives in neurosciences · doi:10.1007/978-3-319-27777-6_5 · Cited by 26
“The probability of finding a connected pair is so low even at short distances (Braitenberg and Schüz, 1998) that existing electrophysiological techniques would seem to be inappropriate for searching for interconnected pairs at larger distances.”
BackgroundCortical Gamma Oscillations: Details of Their Genesis Preclude a Role in Cognition
Merker, B. H. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00078 · Cited by 22
“…hundreds to thousands of other pyramidal cells located near and far in the cortical expanse (for details, see Peters, 1987a,b; Douglas and Martin, 1991; Beaulieu et al., 1992; Braitenberg and Schüz, 1998; Thomson and Lamy, 2007; Harris and Mrsic-Flogel, 2013; Callaway and Luo, 2015, pp. 8982–8983).”
BackgroundWiring Optimization in the Brain
Chklovskii, D. B., & Stevens, C. F. (1999) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) · Cited by 22
Imposing Biological Constraints onto an Abstract Neocortical Attractor Network Model
Johansson, C., & Lansner, A. (2007) · Neural Computation · doi:10.1162/neco.2007.19.7.1871 · Cited by 22
“The average number of synapses per neuron is roughly constant at 8000 in cortices ranging in size from mouse to human (Braitenberg & Schuz, 1998; Beaulieu & Colonnier, 1989; Pakkenberg et al., 2003).”
BackgroundInfluentialRichter, K., Heß, A., & Scheich, H. (1999) · Brain Research · doi:10.1016/s0006-8993(99)01440-7 · Cited by 22
Lehr, A. B., Luboeinski, J., & Tetzlaff, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-22430-7 · Cited by 20
Synapse-type-specific competitive Hebbian learning forms functional recurrent networks
Eckmann, S., Young, E. J., & Gjorgjieva, J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305326121 · Cited by 33
“181, 185 & 186 for ˆ λ † = ˆ λ ∗ ).”
Mishchenko, Y., & Paninski, L. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0390-z · Cited by 21
Dawson, D. A., Lam, J., Lewis, L. B., Carbonell, F., Mendola, J. D., & Shmuel, A. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0331 · Cited by 21
“In addition, the majority of anatomical connections impinging on any cortical neuron are of local origin with distances at the mm scale (Braitenberg and Shuez, 1998).”
BackgroundSynapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture
Knight, J. C., & Furber, S. B. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00420 · Cited by 20
“For the purpose of this paper we concentrate on models of the cortex where anatomical data (Beaulieu and Colonnier, 1989; Pakkenberg et al., 2003; Braitenberg and Schüz, 2013) suggests that, across species, cortical neurons received an average of around 8000 synaptic inputs.”
MethodSIDDIQUI, M., Wedemann, R. S., & Jensen, H. J. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.08.011 · Cited by 21
“The role of excitatory and inhibitory synapses in the brain has been discussed in the literature [43, 44].”
BackgroundMüller, E. J., & Robinson, P. A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006217 · Cited by 21
Agnosic vision is like peripheral vision, which is limited by crowding
Strappini, F., Pelli, D. G., Pace, E. D., & Martelli, M. (2017) · Cortex · doi:10.1016/j.cortex.2017.01.012 · Cited by 22
Forde, N. J., Ronan, L., Zwiers, M. P., Alexander‐Bloch, A. F., Faraone, S. V., Oosterlaan, J., Heslenfeld, D. J., Hartman, C. A., Buitelaar, J. K., & Hoekstra, P. J. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00218 · Cited by 22
“Interestingly, despite the cortex generally being associated with cell bodies rather than connections 95% of connections in the brain are found in the cortex in the form of short-range connections (Braitenberg and Schüz, 1998).”
BackgroundModelling acute and lasting effects of tDCS on epileptic activity
Denoyer, Y., Merlet, I., Wendling, F., & Benquet, P. (2020) · Journal of Computational Neuroscience · doi:10.1007/s10827-020-00745-6 · Cited by 25
Performance/price estimates for cortex-scale hardware: A design space exploration
Zaveri, M., & Hammerstrom, D. W. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.12.003 · Cited by 18
Inflammation Effects on Glutamate as a Pathway to Neuroprogression in Mood Disorders
Haroon, E., & Miller, A. H. (2017) · Modern trends in psychiatry · doi:10.1159/000470805 · Cited by 22
Organization and Priming of Long-term Memory Representations with Two-phase Plasticity
Luboeinski, J., & Tetzlaff, C. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10021-7 · Cited by 19
“The ratio of 4:1 between excitatory and inhibitory neurons is typical for cortical and hippocampal networks [86].”
BackgroundCo-operative Populations of Neurons: Mean Field Models of Mesoscopic Brain Activity
Liley, D. T. J., Foster, B. L., & Bojak, I. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_11 · Cited by 21
Assessment and Modification of the Tinnitus-Related Cortical Network
Schlee, W., Dohrmann, K., Hartmann, T., Lorenz, I., Müller, N., Elbert, T. R., & Weisz, N. (2008) · Seminars in Hearing · doi:10.1055/s-0028-1082033 · Cited by 20
Attractor neural networks with patchy connectivity
Johansson, C., Martin Rehn, A., & Lansner, A. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.002 · Cited by 20
“A striking anatomical feature of the cortex is its homogeneity; a few types of different neurons are orderly arranged in a repetitive pattern throughout the cortex [4,23].”
BackgroundScaling Principles of Distributed Circuits
Srinivasan, S., & Stevens, C. F. (2019) · Current Biology · doi:10.1016/j.cub.2019.06.046 · Cited by 18
Stimulus-driven changes in sensorimotor behavior and neuronal functional connectivity
Rebesco, J. M., & Miller, L. E. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53355-5.00006-3 · Cited by 18
Grades in Neural Complexity: How Large Is the Span?
Bullock, T. H. (2002) · Integrative and Comparative Biology · doi:10.1093/icb/42.4.757 · Cited by 19
A Literature Review: Forest Management with Neural Network and Artificial Intelligence
Imada, A. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-08201-1_3 · Cited by 21
Rakowski, F., & Karbowski, J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005834 · Cited by 18
New Aspects of Axonal Structure and Function
Feldmeyer, D., & Lübke, J. H. R. (2010) · Springer eBooks · doi:10.1007/978-1-4419-1676-1 · Cited by 21
Imaging microstructure in the living human brain: A viewpoint
Paus, T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.013 · Cited by 19
“Braitenberg and Schuz estimated that vessels and cell bodies occupy 13.8% of the mouse cortical volume (Braitenberg and Schuz, 1998).”
BackgroundInfluentialContribution of lateral interactions in V1 to organization of response properties
Wright, J. J., Alexander, D. M., & Bourke, P. D. (2006) · Vision Research · doi:10.1016/j.visres.2006.02.017 · Cited by 19
“…of synapses on V1 neurons are formed by lateral connections arising from neurons with somata distributed over a wide range of cortical separations (Braitenberg & Schuz, 1998) and modeling supports the propagation of signals in a wave-like manner via polysynaptic pathways (e.g., Jirsa & Haken,…”
BackgroundInfluentialScaling in Colloidal and Biological Networks
Nosonovsky, M., & Roy, P. (2020) · Entropy · doi:10.3390/e22060622 · Cited by 19
Kallistatin leads to cognition impairment via downregulating glutamine synthetase
Long, Y., Zhao, Z., Xie, W., Shi, J., Yang, F., Zhu, D., Jiang, P., Tang, Q., Zhou, T., Jiang, B., et al. (2024) · Pharmacological Research · doi:10.1016/j.phrs.2024.107145 · Cited by 17
The neonatal synaptic big bang
Bourgeois, J. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511711848.007 · Cited by 18
Efficient methods for sampling spike trains in networks of coupled neurons
Mishchenko, Y., & Paninski, L. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas467 · Cited by 20
“One of the central goals of neuroscience is to understand how the structure of neural circuits underlies the processing of information in the brain, and in recent years a considerable effort has been focused on measuring neural connectivity empirically [Shepherd, Pologruto and Svoboda (2003); Bureau, Shepherd and Svob…”
BackgroundMethodInfluentialDomain Architecture of a Calcium-Permeable AMPA Receptor in a Ligand-Free Conformation
Midgett, C. R., Gill, A., & Madden, D. R. (2012) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2011.00056 · Cited by 18
“INTRODUCTION Glutamate is the most common excitatory neurotransmitter in the brain, estimated to be responsible for signaling at 80% of cortical synapses (Braitenberg and Schüz, 1998).”
Developmental time windows for axon growth influence neuronal network topology
Lim, S. I., & Kaiser, M. (2015) · Biological Cybernetics · doi:10.1007/s00422-014-0641-3 · Cited by 18
Karbowski, J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004532 · Cited by 18
“A synaptic connection between excitatory neurons, which are majority in the cortex [38], can potentially be generated in that cortical region where axonal and dendritic trees spatially overlap [71].”
Cortical Correlates of the Simulated Viewpoint Oscillation Advantage for Vection
Kirollos, R., Allison, R. S., & Palmisano, S. A. (2017) · Multisensory Research · doi:10.1163/22134808-00002593 · Cited by 18
“Atwell and Iadecola (2002) also report excitatory activity and report that inhibitory neurons are much rarer in the cortex than excitatory neurons (Abeles, 1991; Braitenberg and Schüz, 1998).”
BackgroundNatali, F., Dolce, C., Peters, J., et al. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0186 · Cited by 18
Computational Modeling of Responses in Human Visual Cortex
Wandell, B. A., Winawer, J. A., & Kay, K. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00347-x · Cited by 18
“Human cortex contains 50000 neurons per mm3 so that a typical 2 mm isotropic voxel contains about 400000 neurons (Braitenberg & Schüz, 1998); the 1 mm isotropic voxels depicted in Figure 2 contain about 50000 neurons each.”
BackgroundNeuropsychiatry and quantitative EEG in the 21st Century
Thatcher, R. W. (2011) · Neuropsychiatry · doi:10.2217/npy.11.45 · Cited by 19
On a phase diagram for random neural networks with embedded spike timing dependent plasticity
Turova, T. S., & Villa, A. E. P. (2006) · Biosystems · doi:10.1016/j.biosystems.2006.05.019 · Cited by 17
Anatomical Concepts of Brain Connectivity
Kötter, R. (2007) · Understanding complex systems · doi:10.1007/978-3-540-71512-2_5 · Cited by 18
Inferring network activity from synaptic noise
Rudolph, M., & Alain Destexhe, A. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.015 · Cited by 18
“Thus, ‘‘probing the network’’ through intracellular Vm activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.”
BackgroundSequence Detectors as a Basis of Grammar in the Brain
Friedemann Pulvermüller, A. (2003) · Theory in Biosciences · doi:10.1007/s12064-003-0039-6 · Cited by 21
“If only pyramidal cells, the most common neuron type in the cortex (Braitenberg & Schüz, 1998), are considered and their interactions through glutamate synapses, it may be appropriate to assume that EPSPs have similar decay slopes.”
Computational Anatomical Analysis of the Basal Forebrain Corticopetal System
Záborszky, L., Csordás, A., Buhl, D. L., et al. (2002) · Humana Press eBooks · doi:10.1007/978-1-59259-275-3_9 · Cited by 18
Learning invariant features using inertial priors
Dean, T. (2007) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-006-9039-9 · Cited by 18
“All nodes have roughly the same degree, but the length of edges increases with the level, assuming the obvious threedimensional embedding (see Figure 1.b) and in keeping with the anatomical observations of Braitenberg and Schuz [7].”
ResultAnalysis of Jansen's model of a single cortical column
Grimbert, F., & Faugeras, O. D. (2006) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 18
Liley, D. T. J., & Muthukumaraswamy, S. D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116408 · Cited by 16
Alcohol Induced Brain and Liver Damage: Advantages of a Porcine Alcohol Use Disorder Model
Shin, S. K., Kaiser, E. E., & West, F. D. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2020.592950 · Cited by 21
“…is ∼2.5 mm while those of a pig is ∼2.1 mm as opposed to 0.8– 1.5 mm in rodents, making the extrapolation of porcine data regarding alcohol induced cortical alterations to humans more representative (Mayhew et al., 1996; Braitenberg and Schüz, 1998; Fischl and Dale, 2000; Vetreno et al., 2017).”
BackgroundInfluentialOrganization of Cortical and Thalamic Input to Inhibitory Neurons in Mouse Motor Cortex
Okoro, S. U., Goz, R. U., Njeri, B. W., Harish, M., Ruff, C. F., Ross, S. E., Gerfen, C. R., & Hooks, B. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0950-22.2022 · Cited by 25
Mehta-Pandejee, G., Robinson, P. A., Henderson, J. A., Aquino, K. M., & Sarkar, S. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.03.014 · Cited by 17
A modular attractor associative memory with patchy connectivity and weight pruning
Meli, C., & Lansner, A. (2013) · Network Computation in Neural Systems · doi:10.3109/0954898x.2013.859323 · Cited by 17
“The connectivity and the retrieval procedure can also affect the asymptotic value, but on a smaller scale (Schwenker et al. 1996; Bosch and Kurfess 1998).”
BackgroundRandom waves in the brain: Symmetries and defect generation in the visual cortex
Schnabel, M., Kaschube, M., Löwel, S., & Wolf, F. (2007) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2007-00152-5 · Cited by 17
“In every cubic millimeter 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 [ 4 ].”
BackgroundHigher-Order Brain Network Analysis for Auditory Disease
Li, P., Cai, Y., Wang, C., Liang, M., & Zheng, Y.-Q. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9815-7 · Cited by 17
“Brain region and Node color The brain is roughly divided into 5 cortex regions, including prefrontal cortex, premotor cortex, auditory cortex, somatosensory cortex andvisual cortex [5, 34].”
Background26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
Rubchinsky, L. L., Ahn, S., Klijn, W., Cumming, B., Yates, S., Karakasis, V., Peyser, A., Woodman, M., Diaz-Pier, S., Deraeve, J., et al. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0371-2 · Cited by 17
Lagzi, F., & Fairhall, A. L. (2024) · Science Advances · doi:10.1126/sciadv.adi4350 · Cited by 27
“It is known that the (visual) cortex is composed more of excitatory neurons than inhibitory neurons ( 52 , 53 ); however, recurrent IPSPs are stronger than recurrent EPSPs to maintain a stable, balanced state in the existence of external input ( 39 ).”
Aburano, H., Ueda, F., Yoshie, Y., et al. (2015) · Japanese Journal of Radiology · doi:10.1007/s11604-015-0430-5 · Cited by 16
“With regard to glutamate (Glu), almost 85% of all synapses in the brain are glutamatergic, making Glu a central molecule in the brain [3].”
BackgroundPersonalised modelling with spiking neural networks integrating temporal and static information
Doborjeh, M., Kasabov, N. K., Doborjeh, Z., Enayatollahi, R., Tu, E., & Gandomi, A. H. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.07.021 · Cited by 16
Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms
Galadí, J. A., Torres, J. J., & Marro, J. (2019) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2019.105093 · Cited by 17
“…according to histological data —showing that, in portions of the cortex, there are about four times more excitatory than inhibitory neurons [14, 15, 16, 17]— the E/I ratio is assumed here to be 4, so that any I neuron receives effective excitatory inputs from 32 surrounding E s and any I neuron…”
BackgroundBrain Language Mechanisms Built on Action and Perception
Friedemann Pulvermüller, A., & Fadiga, L. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-407794-2.00026-2 · Cited by 16
From Complex Networks to Intelligent Systems
Sporns, O. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00616-6_2 · Cited by 16
“phylogenetically younger) brains, neuroanatomical investigations have revealed intricate networks that link neurons and neuronal populations at different levels of scale, ranging from single cells at the microscopic level, to mesoscopic structures such as columns, and to macroscopic structural units such as nuclei and…”
BackgroundYu, D., & Parlitz, U. (2010) · Physical Review E · doi:10.1103/physreve.82.026108 · Cited by 16
Experience-Induced Neural Circuits That Achieve High Capacity
Feldman, V., & Valiant, L. G. (2009) · Neural Computation · doi:10.1162/neco.2009.08-08-851 · Cited by 17
“Each neuron is connected to only a very small fraction of the other neurons (Braitenberg & Schuz, 1998).”
BackgroundAvitable, D., & Wedgwood, K. C. A. (2017) · Journal of Mathematical Biology · doi:10.1007/s00285-016-1070-9 · Cited by 15
“In the past decades, single-neuron recordings have been complemented by multineuronal experimental techniques, which have provided quantitative evidence that the cells forming the nervous systems are coupled both structurally [8] and functionally (for a recent review, see [75] and references therein).”
BackgroundFunction and Evolution of the Reptilian Cerebral Cortex
Naumann, R. K., & Laurent, G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00022-1 · Cited by 18
Sequence Detectors as a Basis of Grammar in the Brain
Pulvermüller, F. (2003) · Theory in Biosciences · doi:10.1078/1431-7613-00076 · Cited by 16
A model of surround suppression through cortical feedback
Sullivan, T. J., & de Sa, V. R. (2006) · Neural Networks · doi:10.1016/j.neunet.2005.12.003 · Cited by 16
“This gives a ratio of 4:1 excitatory to inhibitory neurons, which is consistent with physiology (Braitenberg & Schuz, 1998).”
MethodResultCan we observe collective neuronal activity from macroscopic aggregate signals?
Hadjipapas, A., Casagrande, E., Nevado, Á., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.035 · Cited by 16
“…the context of the underlying neuronal population generating macroscopic brain signals, these assumptions hold: coupling among theneurons is given (Braitenberg and Schüz,1998;Hellwig, 2000) and furthermore empirical relationships betweenmacroscopic aggregate signals to collective network…”
BackgroundBraitenberg, V. (2002) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2002.tb07565.x · Cited by 16
Self-citationVisualization of density relations in large-scale neural networks
Nádasdy, Z., & Záborszky, L. (2001) · Anatomy and Embryology · doi:10.1007/s004290100203 · Cited by 16
A Survey of Bio‐Inspired and Other Alternative Architectures
Hammerstrom, D. W. (2010) · Nanotechnology · doi:10.1002/9783527628155.nanotech045 · Cited by 16
“These are subsequently organized into larger columns variously called just “columns”, “cortical columns”, “hypercolumns”, or “modules.” Braitenberg [ 43 ] postulates two general connectivity systems in cortex: “metric” (high density connections to physically local cells, based on actual two-dimensional layout), and “a…”
Background
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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