Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–11 of 11 results
Advanced filters: Author: Viren Jain Clear advanced filters

  • Early machine-learning systems were inspired by neural networks — now AI might allow neuroscientists to get to grips with the brain’s unique complexities.

    • Viren Jain
    Comments & Opinion
    Nature
    Volume: 623, P: 247-250

  • A technique called LICONN (light-microscopy-based connectomics) allows mapping of brain tissue at synapse level and simultaneous measurement of molecular information, thus enabling quantification of cellular properties and multimodal analysis of brain tissue.

    • Mojtaba R. Tavakoli
    • Julia Lyudchik
    • Johann G. Danzl
    ResearchOpen Access
    Nature
    Volume: 642, P: 398-410

  • SegCLR automatically annotates segmented electron microscopy datasets of the brain with information such as cellular subcompartments and cell types, using a self-supervised contrastive learning approach.

    • Sven Dorkenwald
    • Peter H. Li
    • Viren Jain
    ResearchOpen Access
    Nature Methods
    Volume: 20, P: 2011-2020

  • New approaches in artificial intelligence (AI), such as foundation models and synthetic data, are having a substantial impact on many areas of applied computer science. Here we discuss the potential to apply these developments to the computational challenges associated with producing synapse-resolution maps of nervous systems, an area in which major ambitions are currently bottlenecked by AI performance.

    • Michał Januszewski
    • Viren Jain
    Comments & Opinion
    Nature Methods
    Volume: 21, P: 1398-1399

  • Flood-filling networks are a deep-learning-based pipeline for reconstruction of neurons from electron microscopy datasets. The approach results in exceptionally low error rates, thereby reducing the need for extensive human proofreading.

    • Michał Januszewski
    • Jörgen Kornfeld
    • Viren Jain
    Research
    Nature Methods
    Volume: 15, P: 605-610

  • Improved electron microscopy methods are used to map a mammalian retinal circuit of close to 1,000 neurons; the work reveals a new type of retinal bipolar neuron and suggests functional mechanisms for known visual computations.

    • Moritz Helmstaedter
    • Kevin L. Briggman
    • Winfried Denk
    Research
    Nature
    Volume: 500, P: 168-174

  • Volume electron microscopy data of brain tissue can tell us much about neural circuits, but increasingly large data sets demand automation of analysis. Here, the authors introduce cellular morphology neural networks and successfully automate a range of morphological analysis tasks.

    • Philipp J. Schubert
    • Sven Dorkenwald
    • Joergen Kornfeld
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-12

  • SyConn2 is a machine learning-based framework for inferring and analyzing the connectomes contained in a volume electron microscopy dataset of brain tissue, for example from the zebra finch.

    • Philipp J. Schubert
    • Sven Dorkenwald
    • Joergen Kornfeld
    ResearchOpen Access
    Nature Methods
    Volume: 19, P: 1367-1370