This paper rigorously establishes that, by increasing the number of dendritic branches, we can achieve an infinite approximation to any continuous function ...

The purpose of this paper is to prove that the DNM can approximate any continuous function with very accurate accuracy by using the principle of bacN ...

DNM based on the characteristics of dendritic structures [39][40][41] was proposed in [42]. A single neuron as a synapse can be nonlinearly mapped by the ...

Mar 7, 2024 · DNM is a deep neural network model with a dendritic tree structure, which enables it to perform complex logic operations and approximate ...

Jan 17, 2020 · A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed.

Jun 7, 2022 · Research on dendrites has been conducted for decades, providing valuable information for the development of dendritic computation.

More exploitation algorithms are more capable of training neurons to obtain better best results. Compared to DNM, DNM-RP improves accuracy by an average of 9.7% ...

Jun 11, 2021 · Todo, Dendritic Branches of DNM Help to Improve Approximation accuracy, in: 2019 6th International Conference on Systems and Informatics ...

Apr 4, 2024 · A dendritic neuron model (DNM) is a deep ... This work proposes a novel initialization method specifically designed to improve the performance of ...

Mar 20, 2023 · Todo, Dendritic branches of dnm help to improve approximation accu- racy, in 2019 6th International Conference on Systems and Informatics ...