This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
N. Spruston, G. Stuart, M. Häusser, G. Stuart, N. Spruston, M. Häusser, Eds. Principles of Dendritic Integration. – In: Dendrites. Oxford, Oxford University Press, 2016, pp. 351-398.Search in Google Scholar
Richards, S. E. V., S. D. Van Hooser. Neural Architecture: From Cells to Circuits. – Journal of Neurophysiology, Vol. 120, 2018, No 2, pp. 854-866.Search in Google Scholar
Spruston, N. Pyramidal Neurons: Dendritic Structure and Synaptic Integration. – Nat. Rev. Neurosci., Vol. 9, 2008, pp. 206-221.Search in Google Scholar
Zhang, J. Basic Neural Units of the Brain: Neurons, Synapses and Action Potential. – arXiv preprint arXiv:1906.01703, 2019.Search in Google Scholar
Herculano-Houzel, S. The Remarkable, Yet Not Extraordinary, Human Brain as a Scaled-Up Primate Brain and Its Associated Cost. – Proceedings of the National Academy of Sciences, Vol. 109, 2012, pp. 10661-10668.Search in Google Scholar
Chavlis, S., P. Poirazi. Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks. – Current Opinion in Neurobiology, Vol. 70, 2021, pp. 1-10.Search in Google Scholar
McCulloch, W. S., W. Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. – Bulletin of Mathematical Biophysics, Vol. 5, 1943, pp. 115-133.Search in Google Scholar
Rosenblatt, F. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. – Psychological Review, Vol. 65, 1958, pp. 386-408.Search in Google Scholar
Yanling, Z., D. Bimin, W. Zhanrong. Analysis and Study of Perceptron to Solve XOR Problem. – In: Proc. of 2nd International Workshop on Autonomous Decentralized System, 2002, pp. 168-173.Search in Google Scholar
G. Stuart, N. Spruston, M. Häusser, Eds. Dendrites. Oxford, Oxford University Press, 2016.Search in Google Scholar
Wang, Z., S. Gao, J. Wang, H. Yang, Y. Todo. A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm. – Computational Intelligence and Neuroscience, Vol. e2710561, 2020.Search in Google Scholar
Ji, J., S. Gao, J. Cheng, Z. Tang, Y. Todo. An Approximate Logic Neuron Model with a Dendritic Structure. – Neurocomputing, Vol. 173, 2016, pp. 1775-1783.Search in Google Scholar
Marcie, J. T., E. von Hippel. The Situated Nature of Adaptive Learning in Organizations. – Organization Science, Vol. 8, 1997, No 1, pp. 71-83.Search in Google Scholar
Xiao, H., R. Kashif, V. Roland. Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. – arXiv preprint arXiv:1708.07747, 2017.Search in Google Scholar
Fisher, R. A. The Use of Multiple Measurements in Taxonomic Problems. – Annals of Human Genetics, Vol. 7, 1936, pp. 179-188.Search in Google Scholar
Wolberg, W. H., W. N. Street, O. L. Mangasarian. Breast Cancer Wisconsin (Diagnostic) Data Set. – UCI Machine Learning Repository, 1992.Search in Google Scholar
Krizhevsky, A., G. Hinton. Learning Multiple Layers of Features from Tiny Images. – University of Toronto, 2009.Search in Google Scholar
Don, S., D. Chung, K. Revathy, E. Choi, D. Min. A New Approach for Mammogram Image Classification Using Fractal Properties. – Cybernetics and Information Technologies, Vol. 12, 2013, No 2, pp. 69-83.Search in Google Scholar