ArticleNature communications2019
Using slow frame rate imaging to extract fast receptive fields.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- High-speed whole-brain imaging in Drosophila.Nature communications · 2026Article
- Adaptation to visual sparsity enhances responses to isolated stimuli.Current biology : CB · 2024Article
- Current Practice in Using Voltage Imaging to Record Fast Neuronal Activity: Successful Examples from Invertebrate to Mammalian Studies.Biosensors · 2023Review
- A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.Nature neuroscience · 2022Article
- Excitatory and inhibitory neural dynamics jointly tune motion detection.Current biology : CB · 2022Article
- An Autonomous Molecular Bioluminescent Reporter (AMBER) for Voltage Imaging in Freely Moving Animals.Advanced biology · 2021Article
- A minimal synaptic model for direction selective neurons in Drosophila.Journal of vision · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.
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