A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
01 06 2022
01 06 2022
Historique:
received:
11
05
2021
accepted:
19
04
2022
entrez:
1
6
2022
pubmed:
2
6
2022
medline:
7
6
2022
Statut:
epublish
Résumé
Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.
Identifiants
pubmed: 35650191
doi: 10.1038/s41467-022-30214-w
pii: 10.1038/s41467-022-30214-w
pmc: PMC9160074
doi:
Substances chimiques
Calcium
SY7Q814VUP
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3038Informations de copyright
© 2022. The Author(s).
Références
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198
pmcid: 6456269
doi: 10.1038/s41586-018-0654-5
Larkum, M. E., Zhu, J. J. & Sakmann, B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338–341 (1999).
pubmed: 10192334
doi: 10.1038/18686
Larkum, M. E., Waters, J., Sakmann, B. & Helmchen, F. Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. J. Neurosci. 27, 8999–9008 (2007).
pubmed: 17715337
pmcid: 6672209
doi: 10.1523/JNEUROSCI.1717-07.2007
Ledergerber, D. & Larkum, M. E. Properties of layer 6 pyramidal neuron apical dendrites. J. Neurosci. 30, 13031–13044 (2010).
pubmed: 20881121
pmcid: 6633503
doi: 10.1523/JNEUROSCI.2254-10.2010
Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).
pubmed: 26451489
doi: 10.1016/j.cell.2015.09.029
Narayanan, R. T. et al. Beyond columnar organization: cell type-and target layer-specific principles of horizontal axon projection patterns in rat vibrissal cortex. Cereb. Cortex 25, 4450–4468 (2015).
pubmed: 25838038
pmcid: 4816792
doi: 10.1093/cercor/bhv053
Bliss, T. V. P. & Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993).
pubmed: 8421494
doi: 10.1038/361031a0
Bliss, T. V. P. & Lømo, T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356 (1973).
pubmed: 4727084
pmcid: 1350458
doi: 10.1113/jphysiol.1973.sp010273
Lynch, G. S., Dunwiddie, T. & Gribkoff, V. Heterosynaptic depression: a postsynaptic correlate of long-term potentiation. Nature 266, 737–739 (1977).
pubmed: 195211
doi: 10.1038/266737a0
Dunwiddie, T. & Lynch, G. Long-term potentiation and depression of synaptic responses in the rat hippocampus: localization and frequency dependency. J. Physiol. 276, 353–367 (1978).
pubmed: 650459
pmcid: 1282430
doi: 10.1113/jphysiol.1978.sp012239
Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004).
pubmed: 15450156
doi: 10.1016/j.neuron.2004.09.012
Markram, H., Lübke, J., Frotscher, M. & Sakmann, B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997).
pubmed: 8985014
doi: 10.1126/science.275.5297.213
Bi, G.-q & Poo, M.-m Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998).
pubmed: 9852584
pmcid: 6793365
doi: 10.1523/JNEUROSCI.18-24-10464.1998
Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32, 1149–1164 (2001).
pubmed: 11754844
doi: 10.1016/S0896-6273(01)00542-6
Gerstner, W., Kempter, R., van Hemmen, J. L. & Wagner, H. A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996).
pubmed: 8779718
doi: 10.1038/383076a0
Shouval, H. Z., Bear, M. F. & Cooper, L. N. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc. Natl Acad. Sci. USA 99, 10831–10836 (2002).
pubmed: 12136127
pmcid: 125058
doi: 10.1073/pnas.152343099
Rubin, J. E., Gerkin, R. C., Bi, G.-Q. & Chow, C. C. Calcium time course as a signal for spike-timing-dependent plasticity. J. Neurophysiol. 93, 2600–2613 (2005).
pubmed: 15625097
doi: 10.1152/jn.00803.2004
Clopath, C. & Gerstner, W. Voltage and spike timing interact in STDP: a unified model. Front. Synaptic Neurosci. 2 (2010).
Graupner, M. & Brunel, N. Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proc. Natl Acad. Sci. USA 109, 3991–3996 (2012).
pubmed: 22357758
pmcid: 3309784
doi: 10.1073/pnas.1109359109
Jędrzejewska-Szmek, J., Damodaran, S., Dorman, D. B. & Blackwell, K. T. Calcium dynamics predict direction of synaptic plasticity in striatal spiny projection neurons. Eur. J. Neurosci. 45, 1044–1056 (2017).
pubmed: 27233469
doi: 10.1111/ejn.13287
Ebner, C., Clopath, C., Jedlicka, P. & Cuntz, H. Unifying long-term plasticity rules for excitatory synapses by modeling dendrites of cortical pyramidal neurons. Cell Rep. 29, 4295–4307.e6 (2019).
pubmed: 31875541
pmcid: 6941234
doi: 10.1016/j.celrep.2019.11.068
Meissner-Bernard, C., Tsai, M. C., Logiaco, L. & Gerstner, W. Dendritic voltage recordings explain paradoxical synaptic plasticity: a modeling study. Front. Synaptic Neurosci. 12 (2020).
Graupner, M. & Brunel, N. STDP in a bistable synapse model based on CaMKII and associated signaling pathways. PLoS Comput. Biol. 3, e221 (2007).
pubmed: 18052535
pmcid: 2098851
doi: 10.1371/journal.pcbi.0030221
Mäki-Marttunen, T., Iannella, N., Edwards, A. G., Einevoll, G. T. & Blackwell, K. T. A unified computational model for cortical post-synaptic plasticity. Elife 9, e55714 (2020).
pubmed: 32729828
pmcid: 7426095
doi: 10.7554/eLife.55714
Manninen, T., Hituri, K., Kotaleski, J. H., Blackwell, K. T. & Linne, M.-L. Postsynaptic signal transduction models for long-term potentiation and depression. Front. Comput. Neurosci. 4 (2010). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006457/ .
Kotaleski, J. H. & Blackwell, K. T. Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches. Nat. Rev. Neurosci. 11, 239–251 (2010).
pubmed: 20300102
pmcid: 4831053
doi: 10.1038/nrn2807
Lisman, J. A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc. Natl Acad. Sci. USA 86, 9574–9578 (1989).
pubmed: 2556718
pmcid: 298540
doi: 10.1073/pnas.86.23.9574
Nevian, T. & Sakmann, B. Spine Ca
pubmed: 17065442
pmcid: 6674669
doi: 10.1523/JNEUROSCI.1749-06.2006
Graupner, M. & Brunel, N. Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models. Front. Comput. Neurosci. 4 (2010). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2953414/ .
Shouval, H. Z., Wang, S. S.-H. & Wittenberg, G. M. Spike timing dependent plasticity: a consequence of more fundamental learning rules. Front. Comput. Neurosci. 4 (2010). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922937/ .
Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Neocortical LTD via coincident activation of presynaptic NMDA and cannabinoid receptors. Neuron 39, 641–654 (2003).
pubmed: 12925278
doi: 10.1016/S0896-6273(03)00476-8
Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Multiple forms of long-term plasticity at unitary neocortical layer 5 synapses. Neuropharmacology 52, 176–184 (2007).
pubmed: 16895733
doi: 10.1016/j.neuropharm.2006.07.021
Bender, V. A., Bender, K. J., Brasier, D. J. & Feldman, D. E. Two coincidence detectors for spike timing-dependent plasticity in somatosensory cortex. J. Neurosci. 26, 4166–4177 (2006).
pubmed: 16624937
pmcid: 3071735
doi: 10.1523/JNEUROSCI.0176-06.2006
Bear, M. F. & Malenka, R. C. Synaptic plasticity: Ltp and ltd. Curr. Opin. Neurobiol. 4, 389–399 (1994).
pubmed: 7919934
doi: 10.1016/0959-4388(94)90101-5
Larsen, R. S. & Sjöström, P. J. Synapse-type-specific plasticity in local circuits. Curr. Opin. Neurobiol. 35, 127–135 (2015).
pubmed: 26310110
pmcid: 5280068
doi: 10.1016/j.conb.2015.08.001
Sjöström, P. J. & Häusser, M. A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons. Neuron 51, 227–238 (2006).
pubmed: 16846857
doi: 10.1016/j.neuron.2006.06.017
Froemke, R. C., Poo, M.-m & Dan, Y. Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 434, 221–225 (2005).
pubmed: 15759002
doi: 10.1038/nature03366
Letzkus, J. J., Kampa, B. M. & Stuart, G. J. Learning rules for spike timing-dependent plasticity depend on dendritic synapse location. J. Neurosci. 26, 10420–10429 (2006).
pubmed: 17035526
pmcid: 6674691
doi: 10.1523/JNEUROSCI.2650-06.2006
Inglebert, Y., Aljadeff, J., Brunel, N. & Debanne, D. Synaptic plasticity rules with physiological calcium levels. Proc. Natl Acad. Sci. USA 117, 33639–33648 (2020).
pubmed: 33328274
pmcid: 7777146
doi: 10.1073/pnas.2013663117
Markram, H., Lübke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. 500, 409–440 (1997).
pubmed: 9147328
pmcid: 1159394
doi: 10.1113/jphysiol.1997.sp022031
Egger, V., Feldmeyer, D. & Sakmann, B. Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nat. Neurosci. 2, 1098 (1999).
pubmed: 10570487
doi: 10.1038/16026
Rodríguez-Moreno, A. & Paulsen, O. Spike timing-dependent long-term depression requires presynaptic NMDA receptors. Nat. Neurosci. 11, 744 (2008).
pubmed: 18516036
doi: 10.1038/nn.2125
Banerjee, A., González-Rueda, A., Sampaio-Baptista, C., Paulsen, O. & Rodríguez-Moreno, A. Distinct mechanisms of spike timing-dependent LTD at vertical and horizontal inputs onto L2/3 pyramidal neurons in mouse barrel cortex. Physiol. Rep. 2, e00271 (2014).
pubmed: 24760524
pmcid: 4002250
doi: 10.1002/phy2.271
Zilberter, M. et al. Input specificity and dependence of spike timing-dependent plasticity on preceding postsynaptic activity at unitary connections between neocortical layer 2/3 pyramidal cells. Cereb. Cortex 19, 2308–2320 (2009).
pubmed: 19193711
pmcid: 2742592
doi: 10.1093/cercor/bhn247
Ramaswamy, S. et al. The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Front. Neural Circuits 9 (2015). http://journal.frontiersin.org/article/10.3389/fncir.2015.00044/full
Ramaswamy, S. et al. Intrinsic morphological diversity of thick-tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections. J. Physiol. 590, 737–752 (2012).
pubmed: 22083599
doi: 10.1113/jphysiol.2011.219576
Reimann, M. W., King, J. G., Muller, E. B., Ramaswamy, S. & Markram, H. An algorithm to predict the connectome of neural microcircuits. Front. Comput. Neurosci. 9 (2015).
Barros-Zulaica, N. et al. Estimating the readily-releasable vesicle pool size at synaptic connections in the neocortex. Front. Synaptic Neurosci. 11 (2019). https://www.frontiersin.org/articles/10.3389/fnsyn.2019.00029/full .
Chindemi, G. et al. A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex [Data set]. Zenodo (2021). https://doi.org/10.5281/zenodo.6352774
Jahr, C. E. & Stevens, C. F. Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics. J. Neurosci. 10, 3178–3182 (1990).
pubmed: 1697902
pmcid: 6570236
doi: 10.1523/JNEUROSCI.10-09-03178.1990
Vargas-Caballero, M. & Robinson, H. P. C. A slow fraction of Mg
pubmed: 12611983
doi: 10.1152/jn.01038.2002
Vargas-Caballero, M. & Robinson, H. P. C. Fast and slow voltage-dependent dynamics of magnesium block in the NMDA receptor: the asymmetric trapping block model. J. Neurosci. 24, 6171–6180 (2004).
pubmed: 15240809
pmcid: 6729657
doi: 10.1523/JNEUROSCI.1380-04.2004
Magee, J. C. & Johnston, D. Characterization of single voltage-gated Na+ and Ca
pubmed: 7473260
pmcid: 1156600
doi: 10.1113/jphysiol.1995.sp020862
Sabatini, B. L. & Svoboda, K. Analysis of calcium channels in single spines using optical fluctuation analysis. Nature 408, 589–593 (2000).
pubmed: 11117746
doi: 10.1038/35046076
Cornelisse, L. N., Elburg, R. A. Jv, Meredith, R. M., Yuste, R. & Mansvelder, H. D. High speed two-photon imaging of calcium dynamics in dendritic spines: consequences for spine calcium kinetics and buffer capacity. PLoS ONE 2, e1073 (2007).
pubmed: 17957255
pmcid: 2034355
doi: 10.1371/journal.pone.0001073
Sabatini, B. L., Oertner, T. G. & Svoboda, K. The life cycle of Ca
pubmed: 11832230
doi: 10.1016/S0896-6273(02)00573-1
Harris, K. M. & Stevens, J. K. Dendritic spines of CA 1 pyramidal cells in the rat hippocampus: serial electron microscopy with reference to their biophysical characteristics. J. Neurosci. 9, 2982–2997 (1989).
pubmed: 2769375
pmcid: 6569708
doi: 10.1523/JNEUROSCI.09-08-02982.1989
Schikorski, T. & Stevens, C. F. Quantitative fine-structural analysis of olfactory cortical synapses. Proc. Natl Acad. Sci. USA 96, 4107–4112 (1999).
pubmed: 10097171
pmcid: 22428
doi: 10.1073/pnas.96.7.4107
Arellano, J. I., Benavides-Piccione, R., DeFelipe, J. & Yuste, R. Ultrastructure of dendritic spines: correlation between synaptic and spine morphologies. Front. Neurosci. 1 (2007) http://journal.frontiersin.org/article/10.3389/neuro.01.1.1.010.2007/full .
Molnár, G. et al. Human pyramidal to interneuron synapses are mediated by multi-vesicular release and multiple docked vesicles. eLife 5, e18167 (2016).
pubmed: 27536876
pmcid: 4999310
doi: 10.7554/eLife.18167
Holler, S., Köstinger, G., Martin, K. A. C., Schuhknecht, G. F. P. & Stratford, K. J. Structure and function of a neocortical synapse. Nature 1–6 (2021). https://www.nature.com/articles/s41586-020-03134-2 . Publisher: Nature Publishing Group.
Markram, H., Roth, A. & Helmchen, F. Competitive calcium binding: implications for dendritic calcium signaling. J. Comput. Neurosci. 5, 331–348 (1998).
pubmed: 9663555
doi: 10.1023/A:1008891229546
Markram, H. & Tsodyks, M. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Published online: 29 August 1996; https://doi.org/10.1038/382807a0 382, 807–810 (1996). http://www.nature.com/nature/journal/v382/n6594/abs/382807a0.html .
Holderith, N. et al. Release probability of hippocampal glutamatergic terminals scales with the size of the active zone. Nat. Neurosci. 15, 988–997 (2012).
pubmed: 22683683
pmcid: 3386897
doi: 10.1038/nn.3137
Zitzler, E. & Künzli, S. Indicator-based selection in multiobjective search. In International conference on parallel problem solving from nature, 832–842 (Springer, 2004).
Van Geit, W. et al. BluePyOpt: leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinf. 10 (2016). http://journal.frontiersin.org/article/10.3389/fninf.2016.00017/abstract .
Feldmeyer, D., Lübke, J. & Sakmann, B. Efficacy and connectivity of intracolumnar pairs of layer 2/3 pyramidal cells in the barrel cortex of juvenile rats. J. Physiol. 575, 583–602 (2006).
pubmed: 16793907
pmcid: 1819447
doi: 10.1113/jphysiol.2006.105106
Brasier, D. J. & Feldman, D. E. Synapse-specific expression of functional presynaptic NMDA receptors in rat somatosensory cortex. J. Neurosci. 28, 2199–2211 (2008).
pubmed: 18305253
pmcid: 3071744
doi: 10.1523/JNEUROSCI.3915-07.2008
Larsen, R. S., Rao, D., Manis, P. B. & Philpot, B. D. STDP in the Developing Sensory Neocortex. Front. Synaptic Neurosci. 2, (2010). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059680/ .
Borst, J. G. G. The low synaptic release probability in vivo. Trends Neurosci. 33, 259–266 (2010).
pubmed: 20371122
doi: 10.1016/j.tins.2010.03.003
Lisman, J. & Spruston, N. Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nat. Neurosci. 8, 839–841 (2005).
pubmed: 16136666
doi: 10.1038/nn0705-839
Lisman, J. & Spruston, N. Questions about STDP as a general model of synaptic plasticity. Front. Synaptic Neurosci. 2 (2010). https://www.frontiersin.org/articles/10.3389/fnsyn.2010.00140/full . Publisher: Frontiers.
Brandalise, F., Carta, S., Helmchen, F., Lisman, J. & Gerber, U. Dendritic nmda spikes are necessary for timing-dependent associative ltp in ca3 pyramidal cells. Nat. Commun. 7, 13480–13480 (2016).
pubmed: 27848967
pmcid: 5116082
doi: 10.1038/ncomms13480
Cichon, J. & Gan, W.-B. Branch-specific dendritic Ca2+ spikes cause persistent synaptic plasticity. Nature 520, 180–185 (2015). Number: 7546 Publisher: Nature Publishing Group.
pubmed: 25822789
pmcid: 4476301
doi: 10.1038/nature14251
Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).
pubmed: 28883072
pmcid: 7289271
doi: 10.1126/science.aan3846
Clopath, C., Büsing, L., Vasilaki, E. & Gerstner, W. Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nat. Neurosci. 13, 344–352 (2010).
pubmed: 20098420
doi: 10.1038/nn.2479
Frey, U. & Morris, R. G. Synaptic tagging and long-term potentiation. Nature 385, 533–536 (1997).
pubmed: 9020359
doi: 10.1038/385533a0
Sajikumar, S., Morris, R. G. M. & Korte, M. Competition between recently potentiated synaptic inputs reveals a winner-take-all phase of synaptic tagging and capture. Proc. Natl Acad. Sci. USA 111, 12217–12221 (2014).
pubmed: 25092326
pmcid: 4143050
doi: 10.1073/pnas.1403643111
Redondo, R. L. & Morris, R. G. Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12, 17–30 (2011).
pubmed: 21170072
doi: 10.1038/nrn2963
Clopath, C., Ziegler, L., Vasilaki, E., Büsing, L. & Gerstner, W. Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput. Biol 4, e1000248 (2008).
pubmed: 19112486
pmcid: 2596310
doi: 10.1371/journal.pcbi.1000248
Costa, R. P. et al. Synaptic transmission optimization predicts expression loci of long-term plasticity. Neuron 96, 177–189.e7 (2017).
pubmed: 28957667
pmcid: 5626823
doi: 10.1016/j.neuron.2017.09.021
Dodt, H.-U., Eder, M., Frick, A. & Zieglgänsberger, W. Precisely localized LTD in the neocortex revealed by infrared-guided laser stimulation. Science 286, 110–113 (1999).
pubmed: 10506556
doi: 10.1126/science.286.5437.110
Eder, M., Zieglgänsberger, W. & Dodt, H.-U. Neocortical long-term potentiation and long-term depression: site of expression investigated by infrared-guided laser stimulation. J. Neurosci 22, 7558–7568 (2002).
pubmed: 12196579
pmcid: 6758004
doi: 10.1523/JNEUROSCI.22-17-07558.2002
Holthoff, K., Kovalchuk, Y., Yuste, R. & Konnerth, A. Single-shock LTD by local dendritic spikes in pyramidal neurons of mouse visual cortex. J. Physiol. 560, 27–36 (2004).
pubmed: 15319420
pmcid: 1665193
doi: 10.1113/jphysiol.2004.072678
Tazerart, S., Mitchell, D. E., Miranda-Rottmann, S. & Araya, R. A spike-timing-dependent plasticity rule for dendritic spines. Nat. Commun. 11, 1–16 (2020).
doi: 10.1038/s41467-020-17861-7
Tsodyks, M. V. & Markram, H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl Acad. Sci. USA 94, 719–723 (1997).
pubmed: 9012851
pmcid: 19580
doi: 10.1073/pnas.94.2.719
Hruska, M., Henderson, N., Marchand, S. J. L., Jafri, H. & Dalva, M. B. Synaptic nanomodules underlie the organization and plasticity of spine synapses. Nat. Neurosci. 21, 671–682 (2018).
pubmed: 29686261
pmcid: 5920789
doi: 10.1038/s41593-018-0138-9
Petersen, C. C. H., Malenka, R. C., Nicoll, R. A. & Hopfield, J. J. All-or-none potentiation at CA3-CA1 synapses. Proc. Natl Acad. Sci. USA 95, 4732–4737 (1998).
pubmed: 9539807
pmcid: 22559
doi: 10.1073/pnas.95.8.4732
O’Connor, D. H., Wittenberg, G. M. & Wang, S. S.-H. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc. Natl Acad. Sci. USA 102, 9679–9684 (2005).
pubmed: 15983385
pmcid: 1172253
doi: 10.1073/pnas.0502332102
Enoki, R., Hu, Y.-l, Hamilton, D. & Fine, A. Expression of long-term plasticity at individual synapses in hippocampus is graded, bidirectional, and mainly presynaptic: optical quantal analysis. Neuron 62, 242–253 (2009).
pubmed: 19409269
doi: 10.1016/j.neuron.2009.02.026
Kirkwood, A., Lee, H.-K. & Bear, M. F. Co-regulation of long-term potentiation and experience-dependent synaptic plasticity in visual cortex by age and experience. Nature 375, 328–331 (1995).
pubmed: 7753198
doi: 10.1038/375328a0
Kumbhar, P. et al. Coreneuron: an optimized compute engine for the neuron simulator. Front. Neuroinf. 13, 63 (2019).
doi: 10.3389/fninf.2019.00063
Poirazi, P. & Mel, B. W. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29, 779–796 (2001).
pubmed: 11301036
doi: 10.1016/S0896-6273(01)00252-5
Kampa, B. M., Letzkus, J. J. & Stuart, G. J. Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity. Trends Neurosci. 30, 456–463 (2007).
pubmed: 17765330
doi: 10.1016/j.tins.2007.06.010
Schulz, J. M. Synaptic plasticity in vivo: more than just spike-timing? Front. Synaptic Neurosci. 2, 150 (2010).
pubmed: 21423536
pmcid: 3059710
doi: 10.3389/fnsyn.2010.00150
Major, G., Larkum, M. E. & Schiller, J. Active properties of neocortical pyramidal neuron dendrites. Ann. Rev. Neurosci. 36, 1–24 (2013).
pubmed: 23841837
doi: 10.1146/annurev-neuro-062111-150343
Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).
pubmed: 28008068
doi: 10.1126/science.aah6066
Frank, A. C. et al. Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat. Commun. 9, 1–11 (2018).
doi: 10.1038/s41467-017-02751-2
Takahashi, N. et al. Active dendritic currents gate descending cortical outputs in perception. Nat. Neurosci. 1–9 (2020).
Chindemi, G. Towards a unified understanding of synaptic plasticity: parsimonious modeling and simulation of the glutamatergic synapse life-cycle. Ph.D. thesis, (2018).
Rhodes, P. The properties and implications of NMDA spikes in neocortical pyramidal cells. J. Neurosci 26, 6704–6715 (2006).
pubmed: 16793878
pmcid: 6673826
doi: 10.1523/JNEUROSCI.3791-05.2006
Schneggenburger, R., Zhou, Z., Konnerth, A. & Neher, E. Fractional contribution of calcium to the cation current through glutamate receptor channels. Neuron 11, 133–143 (1993).
pubmed: 7687849
doi: 10.1016/0896-6273(93)90277-X
Bartol JR., T. M. et al. Nanoconnectomic upper bound on the variability of synaptic plasticity. eLife 4, e10778 (2015).
doi: 10.7554/eLife.10778
Toharia, P. et al. PyramidalExplorer: a new interactive tool to explore morpho-functional relations of human pyramidal neurons. Front. Neuroanatomy 9 (2016).
Rojo, C. et al. Laminar differences in dendritic structure of pyramidal neurons in the juvenile rat somatosensory cortex. Cerebral Cortex 26, 2811–2822 (2016).
pubmed: 26762857
pmcid: 4869814
doi: 10.1093/cercor/bhv316
Destexhe, A., Contreras, D., Sejnowski, T. J. & Steriade, M. A model of spindle rhythmicity in the isolated thalamic reticular nucleus. J. Neurophysiol. 72, 803–818 (1994).
pubmed: 7527077
doi: 10.1152/jn.1994.72.2.803
Kahl, C. & Günther, M. Complete the correlation matrix. In Breitner, M. H., Denk, G. & Rentrop, P. (eds.) From Nano to Space: Applied Mathematics Inspired by Roland Bulirsch, 229-244 (Springer, Berlin, Heidelberg, 2008).
Hines, M. L. & Carnevale, N. T. The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997).
pubmed: 9248061
doi: 10.1162/neco.1997.9.6.1179
Hunter, J. D. Matplotlib: A 2d graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
doi: 10.1109/MCSE.2007.55
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
pubmed: 32939066
pmcid: 7759461
doi: 10.1038/s41586-020-2649-2
Wes McKinney. Data structures for statistical computing in python. In Stéfan van der Walt & Jarrod Millman (eds.) Proceedings of the 9th Python in Science Conference, 56–61 (2010).
Waskom, M. L. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
doi: 10.21105/joss.03021
Kluyver, T. et al. Jupyter notebooks – a publishing format for reproducible computational workflows. In Loizides, F. & Schmidt, B. (eds.) Positioning and Power in Academic Publishing: Players, Agents and Agendas, 87–90 (IOS Press, 2016).
Chacón, J. E. & Duong, T. Multivariate kernel smoothing and its applications (CRC Press, 2018).
Ness-Cohn, E. & Braun, R. Fasano-franceschini test: an implementation of a 2-dimensional kolmogorov-smirnov test in r (2021). 2106.10539.
Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, 785-794 (ACM, New York, NY, USA, 2016). https://doi.org/10.1145/2939672.2939785 .
Favreau, C. et al. Brayns: Visualizer for large-scale and interactive ray-tracing of neurons (2015). https://github.com/BlueBrain/Brayns .
Abdellah, M. et al. NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics 34, i574–i582 (2018).
pubmed: 29949998
pmcid: 6022592
doi: 10.1093/bioinformatics/bty231