Ariel Davis
Illustrator
From this contributor
Computational and systems neuroscience needs development
Embracing recent advances in developmental biology can drive a new wave of innovation.
Computational and systems neuroscience needs development
Experimentalists versus modelers — whose work has more lasting impact?
My informal analysis of some of neuroscience’s most cited papers from 1999 explores what drives scientific durability.
Experimentalists versus modelers — whose work has more lasting impact?
Name this network: Addressing huge inconsistencies across studies
Entrenched practices have stymied efforts to build a universal taxonomy of functional brain networks. But a new tool to standardize brain-imaging findings could bring us a step closer.
Name this network: Addressing huge inconsistencies across studies
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.