NeuroAI
Recent articles
Advances and insights on the intersection between neuroscience and artificial intelligence
Commentary: Citation graphs may underestimate neuroscience’s influence on AI
An alternative, albeit less quantitative, way to trace this history is through the people who carried ideas between the fields.
Commentary: Citation graphs may underestimate neuroscience’s influence on AI
An alternative, albeit less quantitative, way to trace this history is through the people who carried ideas between the fields.
Commentary: The rising absolute number of neuro-inspired AI papers represents a substantial footprint
Many of the design choices present in the current generation of artificial-intelligence tools parallel purported properties of the brain.
Commentary: The rising absolute number of neuro-inspired AI papers represents a substantial footprint
Many of the design choices present in the current generation of artificial-intelligence tools parallel purported properties of the brain.
What AI can learn from hunger
To enhance AI safety, take a lesson from biological brains; a small set of wants maintains durable control over a much larger cognitive system.
What AI can learn from hunger
To enhance AI safety, take a lesson from biological brains; a small set of wants maintains durable control over a much larger cognitive system.
When we sleep, our brain is filled with spontaneous neural activity. What is it doing?
Daniel Levenstein explains how internally generated brain activity during sleep shapes our cognition, and why NeuroAI is such a powerful approach to understanding the brain.
When we sleep, our brain is filled with spontaneous neural activity. What is it doing?
Daniel Levenstein explains how internally generated brain activity during sleep shapes our cognition, and why NeuroAI is such a powerful approach to understanding the brain.
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.
Explore more from The Transmitter
Remembering Gerry Fischbach, ‘orchestra conductor’ who brought scientists together
Fischbach, who died 25 October, elucidated the dynamic changes in neuromuscular junctions. Beyond the lab, he shaped careers, academic departments and scientific societies.
Remembering Gerry Fischbach, ‘orchestra conductor’ who brought scientists together
Fischbach, who died 25 October, elucidated the dynamic changes in neuromuscular junctions. Beyond the lab, he shaped careers, academic departments and scientific societies.
WILD platform levels up ability to record brain activity, behavior in nature
The customizable rig combines multimodal recording, onboard processing and neuronal manipulation to power more complex experiments.
WILD platform levels up ability to record brain activity, behavior in nature
The customizable rig combines multimodal recording, onboard processing and neuronal manipulation to power more complex experiments.
The relationship between structure and function in cortical networks
Michael Reimann explains how the shape of neurons puts strong constraints on how they are wired in a network, and how this affects their structure-function relationships.
The relationship between structure and function in cortical networks
Michael Reimann explains how the shape of neurons puts strong constraints on how they are wired in a network, and how this affects their structure-function relationships.