Biology subjects

DiGregorio, D. A.

Publications and source records attributed to DiGregorio, D. A..

4 recordsLinked to original sources

bioRxiv2026

Synaptic dynamics as a tunable substrate shaping neuronal activity sequences

A central tenet of cerebellar computation posits that granule cells generate sparse spatiotemporal activity patterns that support precisely timed motor and cognitive outputs. Using high-speed in vivo calcium and glutamate imaging combined with slice electrophysiology, we show that heterogeneous synaptic dynamics transform mossy fiber inputs into temporally sparse, sequential patterns of GC activity. Region-specific differences in MF glutamate release shape GC response duration and sparsity, tuning the temporal statistics of population sequences to match sensorimotor associative learning demands. These findings provide direct evidence for temporally sparse GC sequences and establish synaptic dynamics as a ubiquitous, tunable mechanism structuring neural activity in time.

neuroscience↗

bioRxiv2025

Precise calcium-to-spike inference using biophysical generative models

The intramolecular dynamics of fluorescent calcium indicators distort the relationship between calcium signals and action potentials ("spikes"), hampering efficient spike inference from calcium imaging. To address this problem, we characterized the calcium response kinetics of three widely used indicators, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow measurements and brain slice recordings. We identify previously unreported kinetic features, including use-dependent slowing of fluorescence decay, that introduce systematic errors in linear model-based inference methods. Using these observations, we developed a multistate model of GCaMP and used it to create biophysically-inspired Bayesian Sequential Monte Carlo and machine learning inference models trained on synthetic datasets. These methods outperform existing methods on spike timing accuracy and correlation benchmarks derived from diverse cell types. Our results show that using synthetic data derived from our biophysical model yields a decoder that outperforms even those trained on extensive experimental data. By separating indicator characterization from inference, our framework, Calcium Spike Processing using Integrated Kinetic Estimation and Simulation (C-SPIKES), provides a generalizable strategy applicable to existing and future calcium indicators.

neuroscience↗

bioRxiv2022

High frequency spike inference with particle Gibbs sampling

Calcium-sensitive fluorescent indicators enable monitoring of spiking activity in large neuronal populations in animal models. Despite the plethora of algorithms developed over the past decades, accurate spike-time inference methods for spike rates exceeding 20 Hz are lacking. More importantly, little attention has been devoted to the quantification of statistical uncertainties in spike time estimation, which is essential for assigning confidence levels to inferred spike patterns. To address these challenges, we introduce (1) a statistical model that accounts for bursting neuronal activity and baseline fluorescence modulation and (2) apply a Monte Carlo strategy (particle Gibbs with ancestor sampling) to estimate the joint posterior distribution of spike times and model parameters. Our method is competitive with state-of-the-art supervised and unsupervised algorithms, as evaluated on the CASCADE benchmark datasets. Analysis of fluorescence transients recorded with the ultrafast genetically encoded calcium indicator GCaMP8f demonstrates that our method can resolve interspike intervals as short as 5 ms. Overall, our study describes a Bayesian inference method for detecting neuronal spiking patterns and quantifying their uncertainty. The use of particle Gibbs samplers enables unbiased estimates of spike times and all model parameters, and provides a flexible statistical framework for testing more specific models of calcium indicators.

neuroscience↗

bioRxiv2017

Stability, affinity and chromatic variants of the glutamate sensor iGluSnFR

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter; can detect sub-micromolar to millimolar concentrations of glutamate; and have blue, green or yellow emission profiles. These variants allow in vivo imaging where original-iGluSnFR was too dim, reveal glutamate transients at individual spine heads, and permit kilohertz imaging with inexpensive, powerful fiber lasers.

neuroscience↗