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Updated: Jul 15, 2025
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Automated customization of large-scale spiking network models to neuronal population activity
Shenghao Wu1,2,3, Chengcheng Huang3,4,5, Adam Snyder6,7,8
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Biorxiv : the Preprint Server for Biology
|October 4, 2023
Summary
We developed Spiking Network Optimization using Population Statistics (SNOPS) to create computational models of brain activity. SNOPS customizes spiking neural networks to match complex neural recordings, advancing our understanding of brain function.
Area of Science:
- Computational neuroscience
- Systems neuroscience
Background:
- Computational models are crucial for understanding brain function.
- Spiking neural networks (SNNs) model neuronal biophysics but are complex to configure.
- Current methods struggle to match SNNs to large-scale neural recordings.
Conclusions:
- SNOPS provides an automated method for developing accurate SNN models.
- This approach facilitates deeper insights into neural network function.
- SNOPS aids in understanding how neural networks generate brain function.
