Acharya, J., Basu, A., Legenstein, R., Limbacher, T., Poirazi, P., & Wu, X. (2021). Dendritic Computing: Branching Deeper into Machine Learning. Neuroscience. doi:10.1016/j.neuroscience.2021.10.001
Abstract:
In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used—structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Ministry of Education (MOE) - AcRF Tier 2
Grant Reference no. : MOE2018-T2-2-083
Description:
i) Grant no. 9380132 from City University of Hong Kong
ii) The European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 899265 (ADOPD).
iii) National Natural Science Foundation of China grant No. 62076084
iv) European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant
Agreement FET-OPEN-RIA No 863245 (NEUREKA) and the EINSTEIN Visiting Fellowship of the EINSTEIN
Foundation, Berlin.