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nMNSD-A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift.
Gianluca Susi1,2,3, Luis F Antón-Toro1,2, Fernando Maestú1,2,4
1UPM-UCM Laboratory of Cognitive and Computational Neuroscience, Centro de Tecnologia Biomedica, Madrid, Spain.
Frontiers in Neuroscience
|March 8, 2021
Summary
The generalized multi-neuronal spike sequence detector (nMNSD) enhances biological learning models by processing more complex data. This new architecture achieves state-of-the-art accuracy and efficiency in classification tasks.
Area of Science:
- Computational Neuroscience
- Machine Learning
Background:
- The multi-neuronal spike sequence detector (MNSD) integrates synaptic plasticity and spike latency for biological learning insights.
- Limitations of MNSD include low input cardinality and lack of internal operation visualization.
Purpose of the Study:
- To generalize the MNSD architecture to handle any number of inputs, creating the nMNSD.
- To introduce a novel analysis method, the "trapezoid method," for nMNSD operation.
- To demonstrate the nMNSD's improved capabilities and performance in classification tasks.
Main Methods:
- Developed the generalized multi-neuronal spike sequence detector (nMNSD) architecture.
- Introduced the "trapezoid method" for analyzing nMNSD's response to spike trains.
- Applied nMNSD to a prior classification problem and benchmarked on the MNIST dataset.
Main Results:
- The nMNSD successfully generalized the MNSD architecture to an arbitrary number of inputs.
- The "trapezoid method" provides a reduced approach to analyze nMNSD's recognition mechanisms.
- nMNSD demonstrated improved classification performance and achieved state-of-the-art accuracies on the MNIST database.
Conclusions:
- The nMNSD offers enhanced capabilities for understanding biological learning mechanisms.
- The nMNSD presents significant advantages in time- and energy-efficiency for classification tasks compared to existing methods.
- This generalized architecture opens new avenues for complex pattern recognition in neural systems.
