An approximate logic neuron model with a dendritic structure

Ji, Junkai; Gao, Shangce; Cheng, Jiujun; Tang, Zheng; Todo, Yuki

doi:10.1016/j.neucom.2015.09.052

Cited by 81 publications

(47 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• SDNM has been successfully applied on a number of classification problems, such as XOR [64], cancer diagnosis [44], Iris and Glass datasets [45]. On the contrary, some other dendritic neuron models are not able to solve such nonlinearly separated problems [50] (e.g., the Legenstein-Maass model [65]).…”

Section: Bp-like Learning Methodsmentioning

confidence: 99%

“…On the contrary, some other dendritic neuron models are not able to solve such nonlinearly separated problems [50] (e.g., the Legenstein-Maass model [65]). More importantly, the classifier resulted from SDNM can be easily implemented in hardware [45] using logic circuits.…”

Section: Bp-like Learning Methodsmentioning

confidence: 99%

“…In [44], an error back-propagation (BP) method was used for training SDNM to perform cancer classification tasks. In [45], we demonstrated that SDNM could be approximately realized by using logic NOT, AND and OR operations, corresponding to its dendritic morphology, and thus was suitable for a simple hardware implementation in practice. In this study, we apply SDNM to perform the tourism arrivals forecasting.…”

Section: Single Dendritic Neuron Modelmentioning

confidence: 99%

“…For a specific given task, SDNM is able to identify what type of synapse (excitatory or inhibitory) is needed, where the synapse should be located, which branch of the dendrite is needed, and which one is not needed [42], [43]. This is realized by modeling the synaptic nonlinearity with a sigmoid function, and thus enabling the single neuron to be capable of computing linearly non-separable functions and approximating any complex continuous function [44], [45]. On the other hand, although the tourist arrivals time series apparently is onedimension, it actually contains high-dimensional information and is a result of many factors such as the tourism policy which strongly influences the number of tourists, and thus making the tourism data nonlinear, irregular, and difficult to be predicted.…”

Section: Copyright C 2017 the Institute Of Electronics Information Amentioning

confidence: 99%

“…By taking the nonlinearity of synapses into consideration, a single dendritic neuron model (SDNM) has been proposed in our previous researches [42], [44], [45]. In [42], an unsupervised learning method was proposed for SDNM to learn two-dimensional eight-directionally selective problems.…”

Section: Single Dendritic Neuron Modelmentioning

confidence: 99%

See 4 more Smart Citations

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYWith the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

show abstract

Section: Bp-like Learning Methodsmentioning

confidence: 99%

Section: Bp-like Learning Methodsmentioning

confidence: 99%

Section: Single Dendritic Neuron Modelmentioning

confidence: 99%

Section: Copyright C 2017 the Institute Of Electronics Information Amentioning

confidence: 99%

Section: Single Dendritic Neuron Modelmentioning

confidence: 99%

See 3 more Smart Citations

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders

Jiang

Gao

Wang

et al. 2016

IEEJ Transactions Elec Engng

Self Cite

View full text Add to dashboard Cite

With the number of deaths due to liver diseases increasing steadily in recent years, early detection and treatment of these diseases has been one of the most active research fields using computational intelligence techniques. In this paper, we propose a more realistic single-neuron model with synaptic nonlinearities in a dendritic tree for liver disorder diagnosis. The computation on the neuron is performed as a combination of dimensional reduction and nonlinearity, which has a neuron-pruning function that can remove useless synapses and dendrites during learning, forming a distinct synaptic and dendritic morphology. The nonlinear interactions in a dendrite tree are expressed using the Boolean logic AND (conjunction), OR (disjunction), and NOT (negation), which can be easily implemented in hardware. Furthermore, an error backpropagation (BP) algorithm is used to train the neuron model, and the performance is compared with a traditional BP neural network in terms of accuracy, sensitivity, and specificity. We use the BUPA liver disorder datasets obtained from the UCI Machine Learning Repository to verify the proposed method. Simulation results show promise for the use of this single-neuron model as an effective pattern classification method in liver disorder diagnostics.

show abstract

A Novel Plastic Neural Model with Dendritic Computation for Classification Problems

Dong

Tang

et al. 2020

Intelligent Computing Theories and Application

View full text Add to dashboard Cite

An approximate logic neuron model with a dendritic structure

Cited by 81 publications

References 28 publications

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders

A Novel Plastic Neural Model with Dendritic Computation for Classification Problems

Contact Info

Product

Resources

About