Glutamate indicators with increased sensitivity and tailored deactivation rates DOI Creative Commons
Abhi Aggarwal, Adrian Negrean, Yang Chen

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

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

Язык: Английский

A deep learning framework for automated and generalized synaptic event analysis DOI Creative Commons
Philipp S O’Neill, Martıń Baccino-Calace, Peter Rupprecht

и другие.

eLife, Год журнала: 2025, Номер 13

Опубликована: Март 5, 2025

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring events carry fundamental function and plasticity. However, their stochastic nature low signal-to-noise ratio present major challenges for the reliable consistent analysis. Here, we introduce miniML, a supervised deep learning-based method accurate classification automated detection spontaneous events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event methods in terms both precision recall. enables precise quantification electrophysiological recordings. We demonstrate learning approach generalizes easily diverse preparations, different optical recording techniques, across animal species. provides not only comprehensive robust framework automated, reliable, standardized events, but also opens new avenues high-throughput investigations dysfunction.

Язык: Английский

Процитировано

2
The future of neurotechnology: From big data to translation DOI
Jinhyun Kim, Thomas J. McHugh, Chul Hoon Kim

и другие.

Neuron, Год журнала: 2025, Номер 113(6), С. 814 - 816

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Pericyte Electrical Signalling and Brain Haemodynamics DOI Creative Commons
Thomas A. Longden, Dominic Isaacs

Basic & Clinical Pharmacology & Toxicology, Год журнала: 2025, Номер 136(5)

Опубликована: Март 30, 2025

ABSTRACT Dynamic control of membrane potential lies at the nexus a wide spectrum biological processes, ranging from individual cell secretions to orchestration complex thought and behaviour. Electrical signals in all vascular types (smooth muscle cells, endothelial cells pericytes) contribute haemodynamics energy delivery across spatiotemporal scales throughout tissues. Here, our goal is review synthesize key studies electrical signalling within brain vasculature integrate these with recent data illustrating an important role for pericytes, doing so attempting work towards holistic description blood flow by signalling. We use this as framework generating further questions that we believe are pursue. Drawing parallels signal integration nervous system may facilitate deeper insights into how organized it controls network level.

Язык: Английский

Процитировано

0
A deep learning framework for automated and generalized synaptic event analysis DOI Creative Commons
Philipp S O’Neill, Martıń Baccino-Calace, Peter Rupprecht

и другие.

eLife, Год журнала: 2024, Номер 13

Опубликована: Июнь 28, 2024

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring events carry fundamental function and plasticity. However, their stochastic nature low signal-to-noise ratio present major challenges for the reliable consistent analysis. Here, we introduce miniML, a supervised deep learning-based method accurate classification automated detection spontaneous events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event methods in terms both precision recall. enables precise quantification electrophysiological recordings. We demonstrate learning approach generalizes easily diverse preparations, different optical recording techniques, across animal species. provides not only comprehensive robust framework automated, reliable, standardized events, but also opens new avenues high-throughput investigations dysfunction.

Язык: Английский

Процитировано

3
A deep learning framework for automated and generalized synaptic event analysis DOI Open Access
Philipp S O’Neill, Martıń Baccino-Calace, Peter Rupprecht

и другие.

Опубликована: Фев. 17, 2025

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring events carry fundamental function and plasticity. However, their stochastic nature low signal-to-noise ratio present major challenges for the reliable consistent analysis. Here, we introduce miniML, a supervised deep learning- based method accurate classification automated detection spontaneous events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event methods in terms both precision recall. enables precise quantification electrophysiological recordings. We demonstrate learning approach generalizes easily diverse preparations, different optical recording techniques, across animal species. provides not only comprehensive robust framework automated, reliable, standardized events, but also opens new avenues high-throughput investigations dysfunction.

Язык: Английский

Процитировано

0
Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems DOI Creative Commons

Vojko Pjanovic,

Jacob A. Zavatone-Veth, Paul Masset

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Uncertainty is a fundamental aspect of the natural environment, requiring brain to infer and integrate noisy signals guide behavior effectively. Sampling-based inference has been proposed as mechanism for dealing with uncertainty, particularly in early sensory processing. However, it unclear how reconcile sampling-based methods operational principles higher-order areas, such attractor dynamics persistent neural representations. In this study, we present spiking network model head-direction (HD) system that combines dynamics. To achieve this, derive required interactions perform sampling from large family probability distributions-including variables encoded Poisson noise. We then propose method allows update its estimate current head direction by integrating angular velocity samples-derived inputs-with pull towards circular manifold, thereby maintaining consistent This makes specific, testable predictions about HD can be examined future neurophysiological experiments: predicts correlated subthreshold voltage fluctuations; distinctive short- long-term firing correlations among neurons; characteristic statistics movement activity "bump" representing direction. Overall, our approach extends previous theories on probabilistic neurons, offers novel perspective computations responsible orientation navigation, supports hypothesis combined provide viable framework studying across brain.

Язык: Английский

Процитировано

0
Glutamate indicators with increased sensitivity and tailored deactivation rates DOI Creative Commons
Abhi Aggarwal, Adrian Negrean, Yang Chen

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

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

Язык: Английский

Процитировано

0