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Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

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Learning smooth dendrite morphological neurons by stochastic gradient descent for pattern classification.

Wilfrido Gómez-Flores¹, Humberto Sossa²

¹Centro de Investigación y de Estudios Avanzados del IPN, Unidad Tamaulipas, Parque TECNOTAM, ZIP 87130, Ciudad Victoria, Tamaulipas, Mexico.

Neural Networks : the Official Journal of the International Neural Network Society

|October 19, 2023

Summary

This study introduces a new learning algorithm for dendrite morphological neurons (DMN) using stochastic gradient descent (SGD). The improved DMN model enhances pattern classification performance, offering a competitive alternative to existing methods.

Keywords:

Dendrite morphological neurons Learnable softmax layer Smooth activation functions Spherical dendrites Stochastic gradient descent

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Automatic Identification of Dendritic Branches and their Orientation

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Area of Science:

Computational Neuroscience
Machine Learning
Artificial Intelligence

Background:

Dendrite morphological neurons (DMN) traditionally use a two-stage learning process.
Existing DMN learning methods lack feedback between dendrite position and weight adjustment.
This limitation hinders optimal classification performance.

Purpose of the Study:

To develop an integrated learning algorithm for DMN using stochastic gradient descent (SGD).
To enable feedback for adjusting dendrite centroids and output layer weights simultaneously.
To improve the classification accuracy of DMN models.

Main Methods:

Derived delta rules for adjusting dendrite centroids and output layer weights.
Minimized cross-entropy loss function under an SGD scheme.
Utilized a differentiable smooth maximum activation function for gradient-based learning.

Main Results:

The proposed SGD-based DMN learning algorithm was compared against eight other DMN models and four standard classifiers (SVM, MLP, RF, k-NN).
Performance was evaluated across 81 diverse datasets.
The proposed method demonstrated superior or competitive results compared to existing DMNs and standard classifiers.

Conclusions:

The integrated SGD learning approach effectively improves DMN classification performance.
This method provides a standardized learning framework for DMNs, akin to current artificial neural networks.
The proposed DMN is a viable and effective alternative for pattern classification tasks.