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Scalable and accurate method for neuronal ensemble detection in spiking neural networks
Rubén Herzog1, Arturo Morales2, Soraya Mora3,4
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile.
Plos One
|July 30, 2021
Summary
We developed a new method to detect neuronal ensembles from spiking neuron activity. This tool accurately identifies ensemble activity in synthetic and real retinal data, outperforming existing methods.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Studying neuronal ensembles is crucial for understanding brain function.
- Existing methods for detecting neuronal ensembles have limitations in scalability, accuracy, or ease of use.
Purpose of the Study:
- To introduce a novel, scalable, and accurate method for detecting neuronal ensembles from spiking neuron populations.
- To provide a user-friendly tool for analyzing ensemble activity.
Main Methods:
- Clustering synchronous population activity (population vectors).
- Validation using synthetic data with varied simulation parameters.
- Application to spike trains from retinal ganglion cells recorded via multi-electrode arrays.
Main Results:
- The method accurately detects neuronal ensembles across a wide range of simulation parameters.
- It outperforms current alternative methodologies in detecting ensemble activity.
- Consistent stimuli-evoked and spontaneous ensemble activity were observed in retinal data.
Conclusions:
- The proposed method is a powerful and efficient tool for studying neuronal ensemble activity.
- Early visual system activity may be organized into distinct functional ensembles.
- A Graphical User Interface is provided to enhance accessibility for researchers.
