Improving a cortical pyramidal neuron model’s classification performance on a real-world ecg dataset by extending inputs

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

Article  Google Scholar 

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

Article  Google Scholar 

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

Article  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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.

Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  Google Scholar 

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)

No login
gif