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Neuroscientist Mac Shine interviews researchers whose work traverses traditional boundaries.
How can we fold cellular-level details into whole-brain neuroimaging networks?
I got answers from Bratislav Misic, who is inventing practical ways to connect the brain’s microscopic features with its macroscopic organization.
How can we fold cellular-level details into whole-brain neuroimaging networks?
I got answers from Bratislav Misic, who is inventing practical ways to connect the brain’s microscopic features with its macroscopic organization.
What happens when a histopathologist teams up with computational modelers?
Answers emerge in my chat with Nicola Palomero-Gallagher, a rare example of someone who connects the brain’s microscopic constituents and macroscopic features.
What happens when a histopathologist teams up with computational modelers?
Answers emerge in my chat with Nicola Palomero-Gallagher, a rare example of someone who connects the brain’s microscopic constituents and macroscopic features.
Explore more from The Transmitter
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.