Biology subjects

Antin, B.

Publications and source records attributed to Antin, B..

3 recordsLinked to original sources

bioRxiv2025

DENDRO: Recovery and denoising of whole-tree dendritic voltagefrom 2D voltage movies

The ability to image voltage at high spatiotemporal resolution across an entire dendritic tree would represent a major advance in systems and circuit neuroscience. Recent advances in genetically encoded voltage indicators (GEVIs) have brought this possibility closer to reality. However, due to fundamental tradeoffs between imaging speed, resolution, SNR, and volume, this goal has remained out of reach. Here we develop a computational method that fuses 3D anatomical information with 2D voltage video data, yielding full time-varying 3D voltage estimates. Our method, termed DENDRO, comprises two steps. In step one, we use the anatomical data to build a microscope model which maps from voltages along the tree to observed fluorescence at the imaging plane. By exploiting local spatial smoothness of the voltage signal, we parameterize the voltage signal using a set of local basis functions, which reduces the dimensionality of the problem and allows us to approximately invert the microscope model. This step leverages spatial but not temporal smoothness of the underlying signal and yields noisy 3D estimates. In step two, we train a lightweight self-supervised neural network to perform spatiotemporal denoising of the inferred voltages. On simulated data, we find that DENDRO is able to recover voltages at high accuracy across an entire dendritic tree. On real voltage movies from hippocampal slices, DENDRO recovers known dendritic phenomena at single trial resolution and millisecond time-scales, and allows visualization of backpropagating action potentials in 3D.

neuroscience↗

bioRxiv2023

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization

Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recordings, has the potential to map monosynaptic connectivity at an unprecedented scale. However, optogenetic mapping of nearby connections poses a challenge, due to stimulation artifacts. When the postsynaptic cell expresses opsin, optical excitation can directly induce current in the patched cell, confounding connectivity measurements. This problem is most severe in nearby cell pairs, where synaptic connectivity is often strongest. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are consistent across repeated stimulations at similar laser power. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available at https://github.com/bantin/PhoRC.

neuroscience↗

bioRxiv2022

Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

Discovering how neural computations are implemented in the cortex at the level of monosynaptic connectivity requires probing for the existence of synapses from possibly thousands of presynaptic candidate neurons. Two-photon optogenetics has been shown to be a promising technology for mapping such monosynaptic connections via serial stimulation of neurons with single-cell resolution. However, this approach is limited in its ability to uncover connectivity at large scales because stimulating neurons one-by-one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic neural ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulation of many neurons at once, considerably increasing the rate at which the existence and strength of synapses are screened. We show that this model-based approach, explicitly incorporating known biophysics of optogenetic mapping experiments, is critical for accurately determining synaptic connectivity using compressed sensing. Second, we developed a deep learning method that isolates the postsynaptic response evoked by each stimulus, allowing stimulation to rapidly switch between ensembles without waiting for the postsynaptic response to return to baseline. We then validated our approach by performing large-scale connectivity mapping experiments in slices from layer 2/3 of mouse primary visual cortex. Together, our system increases the throughput of monosynaptic connectivity mapping by an order of magnitude over existing approaches, enabling the acquisition of connectivity maps at speeds needed to discover the synaptic circuitry implementing neural computations.

neuroscience↗