NeuroAI
Recent articles
Advances and insights on the intersection between neuroscience and artificial intelligence
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
The Transmitter’s favorite essays of 2025
Throughout a tumultuous year in science, researchers opined on policy changes and funding uncertainty, as well as scientific trends and the impact of artificial-intelligence tools on the field.
The Transmitter’s favorite essays of 2025
Throughout a tumultuous year in science, researchers opined on policy changes and funding uncertainty, as well as scientific trends and the impact of artificial-intelligence tools on the field.
The Transmitter’s reading list: Six upcoming neuroscience books, plus notable titles in 2025
Dig into an exploration of the fundamental aspects of intelligence, a new textbook about theoretical neuroscience and a memoir about memory research, among other new releases.
The Transmitter’s reading list: Six upcoming neuroscience books, plus notable titles in 2025
Dig into an exploration of the fundamental aspects of intelligence, a new textbook about theoretical neuroscience and a memoir about memory research, among other new releases.
Breaking the jar: Why NeuroAI needs embodiment
Brain function is inexorably shaped by the body. Embracing this fact will benefit computational models of real brain function, as well as the design of artificial neural networks.
Breaking the jar: Why NeuroAI needs embodiment
Brain function is inexorably shaped by the body. Embracing this fact will benefit computational models of real brain function, as well as the design of artificial neural networks.
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.
Dean Buonomano explores the concept of time in neuroscience and physics
He outlines why he thinks integrated information theory is unscientific and discusses how timing is a fundamental computation in brains.
Dean Buonomano explores the concept of time in neuroscience and physics
He outlines why he thinks integrated information theory is unscientific and discusses how timing is a fundamental computation in brains.
Aran Nayebi discusses a NeuroAI update to the Turing test
And he highlights the need to match neural representations across machines and organisms to build better autonomous agents.
Aran Nayebi discusses a NeuroAI update to the Turing test
And he highlights the need to match neural representations across machines and organisms to build better autonomous agents.
Accepting “the bitter lesson” and embracing the brain’s complexity
To gain insight into complex neural data, we must move toward a data-driven regime, training large models on vast amounts of information. We asked nine experts on computational neuroscience and neural data analysis to weigh in.
Accepting “the bitter lesson” and embracing the brain’s complexity
To gain insight into complex neural data, we must move toward a data-driven regime, training large models on vast amounts of information. We asked nine experts on computational neuroscience and neural data analysis to weigh in.
Does the solution to building safe artificial intelligence lie in the brain?
Now is the time to decipher what makes the brain both flexible and dependable—and to apply those lessons to AI—before an unaligned agentic system wreaks havoc.
Does the solution to building safe artificial intelligence lie in the brain?
Now is the time to decipher what makes the brain both flexible and dependable—and to apply those lessons to AI—before an unaligned agentic system wreaks havoc.
Dmitri Chklovskii outlines how single neurons may act as their own optimal feedback controllers
From logical gates to grandmother cells, neuroscientists have employed many metaphors to explain single neuron function. Chklovskii makes the case that neurons are actually trying to control how their outputs affect the rest of the brain.
Dmitri Chklovskii outlines how single neurons may act as their own optimal feedback controllers
From logical gates to grandmother cells, neuroscientists have employed many metaphors to explain single neuron function. Chklovskii makes the case that neurons are actually trying to control how their outputs affect the rest of the brain.
Explore more from The Transmitter
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Romain Brette reveals fundamental flaws in commonly assumed neuroscience concepts
His new book, “The Brain, In Theory,” offers alternatives to many of the computer science frameworks currently driving theoretical neuroscience.
Romain Brette reveals fundamental flaws in commonly assumed neuroscience concepts
His new book, “The Brain, In Theory,” offers alternatives to many of the computer science frameworks currently driving theoretical neuroscience.