Work

Author response: High frequency spike inference with particle Gibbs sampling

Year: 2025

Type: peer-review

Abstract: This study presents a valuable contribution by introducing a model-based, Bayesian method for inferring action potentials from calcium imaging data that directly quantifies uncertainty in spike timing through posterior distributions.Using a Monte Carlo particle Gibbs sampling approach, the method achieves temporal resolution and accuracy comparable to existing techniques while offering the key added benefit of principled uncertainty estimates.The underlying methodology and characterization are convincing, and the work will be of particular interest to theoretically oriented neuroscientists seeking rigorous new tools for data-driven parameter inference.

Source: eLife

DOI: 10.7554/elife.94723.2.sa0

Authors: Giovanni Diana, B Semihcan Sermet, Gerard J Broussard, Samuel S.-H Wang, David A DiGregorio

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funders: European Commission, Agence Nationale de la Recherche

Award: Statistical Inference of the Cerebellar Network

OpenAlex ID: W4412699558

Cites:

Cited by:

Related to: 10