About this Research Topic
These problems become increasingly critical with gradually understanding of the distributed neural networks at a systematic level. Computational imaging systems with spatiotemporal multiplexing and new fluorescence indicators can increase the limited space-bandwidth product of traditional microscopy with reduced signal crosstalk, while existing methods are still quite expensive or complicated, which cannot be generally accessible for the broad biology community. Deep-learning-based algorithms cannot only enhance the imaging performance with better resolution or signal-to-noise ratio, but also facilitate efficient large-scale data analysis such as motion registrations and calcium extractions. Larger datasets and better evaluation metrics are required to guarantee the robust and reliable performance of deep neural networks in diverse imaging modalities and environments. Unsupervised learning may be a good choice, since it’s usually hard to obtain the ground truth data for functional imaging. More is different, and more is also hard to understand. Mesoscale data at single neuron resolution obtained either by microscopes or electrophysiology usually has a much higher dimensionality than the data of clustering of neurons. With the limited number of trials for stimulus, new models or methods are required to infer the mesoscale functional network connectivity between the neurons to reconstruct the calculating strategy the neural circuit uses to accomplish specific signal processing missions.
For this research topic, we focus on both hardware and software towards the recording, analysis, and modeling of mesoscale neural activities, which may promote the systematic understanding of signal processing procedures in neural networks. We welcome submissions of Original Research, Brief Research Reports, reviews, Mini-Reviews and Opinions on the following topics:
- High-throughput imaging systems or electrophysiology
- Algorithms or datasets for large-scale high-fidelity extraction of the neural activities
- Evaluation metrics or datasets for the data fidelity
- Evaluation metrics or datasets for the data-fidelity
- Algorithms for functional mapping or functional circuit identification
- Models for brain-wide functional network at single neuron level
- Network analysis techniques to find more fundamental arithmetic units or their hierarchical structures of neural network.
Keywords: Recording, Data integration, Mesoscale neural activity, Network analysis, Functional circuits identification
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