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Classification of Signals

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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A supervised learning rule for classification of spatiotemporal spike patterns.

Lilin Guo, Zhenzhong Wang, Malek Adjouadi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 9, 2017

Summary

This study presents a new supervised learning algorithm for spiking neurons, enhancing classification accuracy by incorporating synaptic and axonal delays. The novel method outperforms existing techniques like SPAN and Tempotron for precise spike-based classification.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Area of Science:

Computational neuroscience
Machine learning

Background:

Spiking neural networks (SNNs) are biologically plausible models of neural computation.
Incorporating biological features like synaptic and axonal delays is crucial for realistic SNNs.
Existing supervised learning rules for SNNs often do not fully account for these temporal dynamics.

Purpose of the Study:

To introduce a novel supervised learning algorithm for spiking neurons that explicitly models synaptic and axonal delays.
To evaluate the algorithm's effectiveness in classification tasks using precisely timed spike sequences.
To compare the proposed algorithm's performance against established methods like SPAN and Tempotron.

Main Methods:

Development of a supervised learning algorithm for spiking neurons.
Integration of synaptic delays, axonal delays, and spike-timing-dependent plasticity (STDP).
Focus on the classification capabilities of the trained spiking neurons.

Main Results:

The proposed algorithm enables spiking neurons to classify categories based on precise spike sequences.
Simulation results demonstrate significantly improved classification accuracy compared to SPAN and Tempotron.
The algorithm effectively leverages biologically inspired temporal properties for enhanced performance.

Conclusions:

The novel supervised learning algorithm offers a significant advancement in training spiking neural networks for classification.
Accounting for synaptic and axonal delays is critical for improving SNN classification accuracy.
This method provides a promising approach for developing more sophisticated and biologically realistic neural computation models.