Biology subjects

Arthur, B. J.

Publications and source records attributed to Arthur, B. J..

7 recordsLinked to original sources

bioRxiv2025

iGABASnFR2: Improved genetically encoded protein sensors of GABA

Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However, the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.1-fold improved sensitivity and 30% faster rise time, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance when compared directly to the original sensor. iGABASnFR2 enabled the first measurements of direction-selective GABA release in the retina. In vivo imaging in somatosensory cortex revealed that iGABASnFR2 can report volume-transmitted GABA release following whisker stimulation. Overall, the improved sensitivity and kinetics of iGABASnFR2 make it a more effective tool for imaging GABAergic transmission in intact neural circuits.

neuroscience↗

bioRxiv2025

Glutamate indicators with increased sensitivity and tailored deactivation rates

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neurons thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

neuroscience↗

bioRxiv2022

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1000 times faster than an unoptimized reference CPU implementation. We demonstrate the codes utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.

neuroscience↗

bioRxiv2021

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator

The ability to optically image cellular transmembrane voltage at millisecond-timescale resolution can offer unprecedented insight into the function of living brains in behaving animals. The chemigenetic voltage indicator Voltron is bright and photostable, making it a favorable choice for long in vivo imaging of neuronal populations at cellular resolution. Improving the voltage sensitivity of Voltron would allow better detection of spiking and subthreshold voltage signals. We performed site saturation mutagenesis at 40 positions in Voltron and screened for increased {Delta}F/F0 in response to action potentials (APs) in neurons. Using a fully automated patch-clamp system, we discovered a Voltron variant (Voltron.A122D) that increased the sensitivity to a single AP by 65% compared to Voltron. This variant (named Voltron2) also exhibited approximately 3-fold higher sensitivity in response to sub-threshold membrane potential changes. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, with lower baseline fluorescence. Introducing the same A122D substitution to other Ace2 opsin-based voltage sensors similarly increased their sensitivity. We show that Voltron2 enables improved sensitivity voltage imaging in mice, zebrafish and fruit flies. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

neuroscience↗

bioRxiv2021

SongExplorer: A deep learning workflow for discovery and segmentation of animal acoustic communication signals

Many animals produce distinct sounds or substrate-borne vibrations, but these signals have proved challenging to segment with automated algorithms. We have developed SongExplorer, a web-browser based interface wrapped around a deep-learning algorithm that supports an interactive workflow for (1) discovery of animal sounds, (2) manual annotation, (3) supervised training of a deep convolutional neural network, and (4) automated segmentation of recordings. Raw data can be explored by simultaneously examining song events, both individually and in the context of the entire recording, watching synced video, and listening to song. We provide a simple way to visualize many song events from large datasets within an interactive low-dimensional visualization, which facilitates detection and correction of incorrectly labelled song events. The machine learning model we implemented displays higher accuracy than existing heuristic algorithms and similar accuracy as two expert human annotators. We show that SongExplorer allows rapid detection of all song types from new species and of novel song types in previously well-studied species.

animal behavior and cognition↗

bioRxiv2019

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

neuroscience↗

bioRxiv2017

Neural Changes Underlying Rapid Fly Song Evolution

The neural basis for behavioural evolution is poorly understood. Functional comparisons of homologous neurons may reveal how neural circuitry contributes to behavioural evolution, but homologous neurons cannot be identified and manipulated in most taxa. Here, we compare the function of homologous courtship song neurons by exporting neurogenetic reagents that label identified neurons in Drosophila melanogaster to D. yakuba. We found a conserved role for a cluster of brain neurons that establish a persistent courtship state. In contrast, a descending neuron with conserved electrophysiological properties drives different song types in each species. Our results suggest that song evolved, in part, due to changes in the neural circuitry downstream of this descending neuron. This experimental approach can be generalized to other neural circuits and therefore provides an experimental framework for studying how the nervous system has evolved to generate behavioural diversity.

evolutionary biology↗