Amit, D. J., Wong, K. Y. M., & Campbell, C. (1989). Perceptron learning with sign-constrained weights. Journal of Physics A: Mathematical and General, 22(12), 2039–2045. https://doi.org/10.1088/0305-4470/22/12/009
Bicknell, B. A., & Häusser, M. (2021). A synaptic learning rule for exploiting nonlinear dendritic computation. Neuron, 109(24), 4001-4017.e10. https://doi.org/10.1016/j.neuron.2021.09.044
Article CAS PubMed PubMed Central Google Scholar
Braganza, O., & Beck, H. (2018). The circuit motif as a conceptual tool for multilevel neuroscience. Trends in Neurosciences, 41(3), 128–136. https://doi.org/10.1016/j.tins.2018.01.002
Article CAS PubMed Google Scholar
Chapeton, J., Fares, T., LaSota, D., et al. (2012). Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons. Proceedings of the National Academy of Sciences, 109(51), E3614–E3622. https://doi.org/10.1073/pnas.1211467109
Galloni, A.R., Laffere, A., Rancz, E. (2020). Apical length governs computational diversity of layer 5 pyramidal neurons. eLife, 9:e55. https://doi.org/10.7554/elife.55761
Gerstner, W., & Kistler, W. M. (2002). Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press. https://doi.org/10.1017/cbo9780511815706
Gerstner, W., Kistler, W. M., Naud, R., et al. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press. https://doi.org/10.1017/cbo9781107447615
Gidon, A., Zolnik, T. A., Fidzinski, P., et al. (2020). Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science, 367(6473), 83–87. https://doi.org/10.1126/science.aax6239
Article CAS PubMed Google Scholar
Gütig, R., & Sompolinsky, H. (2006). The tempotron: A neuron that learns spike timing–based decisions. Nature Neuroscience, 9(3), 420–428. https://doi.org/10.1038/nn1643
Article CAS PubMed Google Scholar
Hay, E., Hill, S., Schürmann, F., et al. (2011). Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Computational Biology 7(7):e1002. https://doi.org/10.1371/journal.pcbi.1002107
Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179–1209. https://doi.org/10.1162/neco.1997.9.6.1179
Article CAS PubMed Google Scholar
Hines, M. L., Davison, A. P., & Muller, E. (2009). NEURON and Python. Frontiers in Neuroinformatics, 3, 1. https://doi.org/10.3389/neuro.11.001.2009
Hinton, G.E., Roweis, S. (2002). Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, vol 15. MIT Press, pp 857–864, https://proceedings.neurips.cc/paper_files/paper/2002/file/6150ccc6069bea6b5716254057a194ef-Paper.pdf
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764
Article CAS PubMed PubMed Central Google Scholar
Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience. The MIT Press. https://doi.org/10.7551/mitpress/2526.001.0001
Katz, Y., Menon, V., Nicholson, D. A., et al. (2009). Synapse distribution suggests a two-stage model of dendritic integration in CA1 pyramidal neurons. Neuron, 63(2), 171–177. https://doi.org/10.1016/j.neuron.2009.06.023
Article CAS PubMed PubMed Central Google Scholar
Lapicque, L. (1907). Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization. Journal de physiologie et de pathologie générale, 9, 620–635.
Legenstein, R., Maass, W. (2011). Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. Journal of Neuroscience 31(30):10,787–10,802. https://doi.org/10.1523/jneurosci.5684-10.2011
Legenstein, R., Naeger, C., & Maass, W. (2005). What can a neuron learn with spike-timing-dependent plasticity? Neural Computation, 17(11), 2337–2382. https://doi.org/10.1162/0899766054796888
Limbacher, T., & Legenstein, R. (2020). Emergence of stable synaptic clusters on dendrites through synaptic rewiring. Frontiers in Computational Neuroscience, 14, 57. https://doi.org/10.3389/fncom.2020.00057
Article PubMed PubMed Central Google Scholar
London, M., & Häusser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28(1), 503–532. https://doi.org/10.1146/annurev.neuro.28.061604.135703
Article CAS PubMed Google Scholar
van der Maaten, L., Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(11):2579–2605. http://jmlr.org/papers/v9/vandermaaten08a.html
Magee, J. C. (2000). Dendritic integration of excitatory synaptic input. Nature Reviews Neuroscience, 1(3), 181–190. https://doi.org/10.1038/35044552
Article CAS PubMed Google Scholar
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/bf02478259
Moldwin, T., & Segev, I. (2020). Perceptron learning and classification in a modeled cortical pyramidal cell. Frontiers in Computational Neuroscience, 14, 33. https://doi.org/10.3389/fncom.2020.00033
Article PubMed PubMed Central Google Scholar
Monteiro, J., Pedro, A., & Silva, A. J. (2021). A Gray Code model for the encoding of grid cells in the Entorhinal Cortex. Neural Computing and Applications, 34(3), 2287–2306. https://doi.org/10.1007/s00521-021-06482-w
Moody, G., & Mark, R. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50. https://doi.org/10.1109/51.932724
Article CAS PubMed Google Scholar
Poirazi, P., & Mel, B. W. (2001). Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron, 29(3), 779–796. https://doi.org/10.1016/s0896-6273(01)00252-5
Article CAS PubMed Google Scholar
Poirazi, P., Brannon, T., & Mel, B. W. (2003). Pyramidal neuron as two-layer neural network. Neuron, 37(6), 989–999. https://doi.org/10.1016/s0896-6273(03)00149-1
Article CAS PubMed Google Scholar
Polsky, A., Mel, B. W., & Schiller, J. (2004). Computational subunits in thin dendrites of pyramidal cells. Nature Neuroscience, 7(6), 621–627. https://doi.org/10.1038/nn1253
Article CAS PubMed Google Scholar
Rao. A., Legenstein, R., Subramoney, A., et al. (2021). Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model. bioRxiv https://doi.org/10.1101/2021.03.04.433822
Rosenblatt, F. (1957). The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory
Shai, A.S., Anastassiou, C.A., Larkum, M.E., et al. (2015). Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: Coincidence detection through bursting. PLOS Computational Biology, 11(3):e1004. https://doi.org/10.1371/journal.pcbi.1004090
Sidiropoulou, K., Pissadaki, E. K., & Poirazi, P. (2006). Inside the brain of a neuron. EMBO reports, 7(9), 886–892. https://doi.org/10.1038/sj.embor.7400789
Article CAS PubMed PubMed Central Google Scholar
Song, S., Sjöström, P. J., Reigl, M., et al. (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biology, 3(3), e68. https://doi.org/10.1371/journal.pbio.0030068
Article CAS PubMed PubMed Central Google Scholar
Spruston, N. (2008). Pyramidal neurons: Dendritic structure and synaptic integration. Nature Reviews Neuroscience, 9(3), 206–221. https://doi.org/10.1038/nrn2286
Article CAS PubMed Google Scholar
Ujfalussy, B. B., Makara, J. K., Lengyel, M., et al. (2018). Global and multiplexed dendritic computations under in vivo-like conditions. Neuron, 100(3), 579-592.e5. https://doi.org/10.1016/j.neuron.2018.08.032
Comments (0)