Year: 2025
Type: preprint
Abstract: The intramolecular dynamics of fluorescent indicators of neural activity can distort the accurate estimate of action potential (“spike”) times. In order to develop a more accurate spike inference algorithm we characterized the kinetic responses to calcium of three popular indicator proteins, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow and brain slice recordings. jGCaMP8f showed a use-dependent slowing of fluorescence responses that caused existing inference methods to generate numerous false positives. From these data we developed a multistate model of GCaMP and used it to create Bayesian Sequential Monte Carlo (BiophysSMC) and machine learning (BiophysML) inference methods that reduced false positives substantially. This biophysical method dramatically improved spike time accuracy, detecting individual spikes with a median uncertainty of 4 milliseconds, a performance level that reached the theoretical limit and is twice as accurate as any previous method. Our framework thus highlights advantages of physical model-based approaches over model-free algorithms.
Source: Research Square
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Awards: G3010527862, Synaptic basis of predictive coding in the cerebellar cortex, G4656594441, Use of Calcium Indicator Proteins in Spike Counting Mode, Mechanisms of neural circuit dynamics in working memory anddecision-making, G6803667113, Contribution of somatic and dendritic targeting cerebellar interneurons to temporal coding of sensory context, Adaptive Cerebellar Processing at Cellular Resolution in Flexible Behavior, Nanoscale neuro-anatomical determinants of synaptic plasticity and dendritic integration
OpenAlex ID: W4409751354