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Rohan Gala

Researcher at Allen Institute for Brain Science

Publications -  24
Citations -  528

Rohan Gala is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 8, co-authored 20 publications receiving 266 citations. Previous affiliations of Rohan Gala include Allen Institute for Artificial Intelligence & Northeastern University.

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Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells.

TL;DR: 28 met- types are defined that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability, and layer-specific axon innervation pattern is identified as a defining feature distinguishing different met-types.
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Single-cell transcriptomic evidence for dense intracortical neuropeptide networks

TL;DR: Here, neuron-type-specific patterns of NP gene expression are used to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.
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Active learning of neuron morphology for accurate automated tracing of neurites.

TL;DR: The results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.
Posted ContentDOI

Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons

TL;DR: This work characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons, finding that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features.
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Consistent cross-modal identification of cortical neurons with coupled autoencoders

TL;DR: This work presents an optimization framework to learn coordinated representations of multimodal data and applies it to a large multimodals dataset profiling mouse cortical interneurons, revealing strong alignment between transcriptomic and electrophysiological characterizations and enabling accurate cross-modal data prediction.