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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
Reconstruction of underlying nonlinear deterministic dynamics embedded in noisy spike trains
Yoshiyuki Asai1, Alessandro E P Villa
1The Center for Advanced Medical Engineering and Informatics, Osaka University, Toyonaka, Osaka 560-8531, Japan. asai@bpe.es.osaka-u.ac.jp
This study introduces a new method to denoise neuronal spike trains, effectively filtering out observational noise and missed detections. The pattern grouping algorithm (PGA) successfully reconstructs original temporal dynamics, improving spike train analysis.
Area of Science:
- Computational Neuroscience
- Signal Processing
- Dynamical Systems Theory
Background:
- Neuronal spike trains are susceptible to observational noise, including false positives and missed detections.
- Temporal jitter in spike timing can further obscure underlying neural dynamics.
- Recurrent temporal patterns are hypothesized to represent robust expressions of dynamic processes within spike trains.
Purpose of the Study:
- To develop and evaluate a procedure for denoising neuronal spike trains.
- To filter embedded observational noise while preserving essential temporal features.
- To test the hypothesis that recurrent spike patterns reflect underlying deterministic dynamics.
Main Methods:
- A pattern grouping algorithm (PGA) was applied to simulated noisy spike trains.
- Spike trains were generated from nonlinear deterministic dynamical systems with added noise.
- Three new indices were defined to assess the performance of the denoising procedure.
Main Results:
- The denoising procedure successfully retrieved relevant temporal features of the original dynamics.
- Spurious events (false positives) had a greater negative impact on performance than missed detections (false negatives).
- A strict spike detection criterion can enhance the applicability of PGA for uncovering masked dynamics.
Conclusions:
- The proposed denoising method effectively filters noise in spike trains, revealing underlying deterministic dynamics.
- Reducing spurious spikes through stricter detection criteria is crucial for successful application of PGA.
- This approach offers a promising avenue for analyzing neural information encoded in spike train patterns.
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