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Related Experiment Video

Updated: Nov 2, 2025

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

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

Published on: March 25, 2014

Event-Based Trajectory Prediction Using Spiking Neural Networks.

Guillaume Debat¹, Tushar Chauhan¹, Benoit R Cottereau¹

¹CERCO UMR 5549, CNRS-Université Toulouse 3, Toulouse, France.

Frontiers in Computational Neuroscience

|June 10, 2021

Summary

This summary is machine-generated.

This study combines event-based sensors with spiking neural networks (SNNs) for efficient, real-time artificial vision. The system learns motion features unsupervised and predicts ball trajectories, showcasing bio-inspired AI capabilities.

Keywords:

SNN STDP ball trajectory prediction motion selectivity spiking camera unsupervised learning

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Last Updated: Nov 2, 2025

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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:

Artificial Intelligence
Computational Neuroscience
Computer Vision

Background:

Event-based sensors offer high temporal resolution and low power consumption for vision tasks.
Spiking neural networks (SNNs) mimic biological neural processing for efficient computation.
Combining these technologies creates bio-inspired artificial vision systems.

Purpose of the Study:

To investigate the efficacy of a hybrid event-based camera and SNN system.
To demonstrate unsupervised learning of motion features by SNNs.
To enable real-time ball trajectory prediction using SNNs.

Main Methods:

Utilized a novel hybrid event-based camera for data acquisition.
Employed a multi-layer SNN trained with spike-timing-dependent plasticity (STDP).
Integrated a polynomial regression read-out layer for supervised trajectory prediction.

Main Results:

Neurons exhibited unsupervised learning of spatio-temporal patterns.
Learned neurons became selective to motion features like direction and speed.
The system successfully predicted ball trajectories with high accuracy.

Conclusions:

SNNs coupled with event-based sensors can effectively extract spatio-temporal motion patterns.
This bio-inspired approach enables efficient real-time processing and prediction tasks.
The system demonstrates potential for advanced robotic vision and autonomous systems.