Collaboration
Recent articles
How to be a multidisciplinary neuroscientist
Neuroscience subfields are often siloed. Embracing an integrative approach during training can help change that.
How to be a multidisciplinary neuroscientist
Neuroscience subfields are often siloed. Embracing an integrative approach during training can help change that.
Should I work with these people? A guide to collaboration
Kevin Bender offers advice for early-career neuroscientists on how to choose the right collaborations and avoid the bad ones.
Should I work with these people? A guide to collaboration
Kevin Bender offers advice for early-career neuroscientists on how to choose the right collaborations and avoid the bad ones.
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.