Machine learning
Recent articles
How neuroscientists are using AI
Eight researchers explain how they are using large language models to analyze the literature, brainstorm hypotheses and interact with complex datasets.
How neuroscientists are using AI
Eight researchers explain how they are using large language models to analyze the literature, brainstorm hypotheses and interact with complex datasets.
Competition seeks new algorithms to classify social behavior in animals
The winner of the competition, which launched today and tests contestants’ models head to head, is set to take home $20,000, according to co-organizer Ann Kennedy.
Competition seeks new algorithms to classify social behavior in animals
The winner of the competition, which launched today and tests contestants’ models head to head, is set to take home $20,000, according to co-organizer Ann Kennedy.
This paper changed my life: Dan Goodman on a paper that reignited the field of spiking neural networks
Friedemann Zenke’s 2019 paper, and its related coding tutorial SpyTorch, made it possible to apply modern machine learning to spiking neural networks. The innovation reinvigorated the field.
This paper changed my life: Dan Goodman on a paper that reignited the field of spiking neural networks
Friedemann Zenke’s 2019 paper, and its related coding tutorial SpyTorch, made it possible to apply modern machine learning to spiking neural networks. The innovation reinvigorated the field.
Xaq Pitkow shares his principles for studying cognition in our imperfect brains and bodies
Pitkow discusses how evolution's messy constraints shape optimal brain algorithms, from Bayesian inference to ecological affordances.
Xaq Pitkow shares his principles for studying cognition in our imperfect brains and bodies
Pitkow discusses how evolution's messy constraints shape optimal brain algorithms, from Bayesian inference to ecological affordances.
Should neuroscientists ‘vibe code’?
Researchers are developing software entirely through natural language conversations with advanced large language models. The trend is transforming how research gets done—but it also presents new challenges for evaluating the outcomes.
Should neuroscientists ‘vibe code’?
Researchers are developing software entirely through natural language conversations with advanced large language models. The trend is transforming how research gets done—but it also presents new challenges for evaluating the outcomes.
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.
Many students want to learn to use artificial intelligence responsibly. But their professors are struggling to meet that need.
Effectively teaching students how to employ AI in their writing assignments requires clear guidelines—and detailed, case-specific examples.
Many students want to learn to use artificial intelligence responsibly. But their professors are struggling to meet that need.
Effectively teaching students how to employ AI in their writing assignments requires clear guidelines—and detailed, case-specific examples.
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.
Connectomics 2.0: Simulating the brain
With a complete fly connectome in hand, researchers are taking the next step to model how brain circuits fuel function.
Connectomics 2.0: Simulating the brain
With a complete fly connectome in hand, researchers are taking the next step to model how brain circuits fuel function.
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
Perimenopause: An important—and understudied—transition for the brain
Many well-known perimenopause symptoms arise in the brain, but we still know little about the specific mechanisms at play. More research—in both animals and humans—is essential.
Perimenopause: An important—and understudied—transition for the brain
Many well-known perimenopause symptoms arise in the brain, but we still know little about the specific mechanisms at play. More research—in both animals and humans—is essential.
A community-designed experiment tests open questions in predictive processing
More than 50 scientists came together to identify the key missing data needed to rigorously test theoretical models.
A community-designed experiment tests open questions in predictive processing
More than 50 scientists came together to identify the key missing data needed to rigorously test theoretical models.
‘Neuroethics: The Implications of Mapping and Changing the Brain,’ an excerpt
In his new book, published today, philosopher Walter Glannon examines the ethics of six areas of neuroscience. In Chapter 4, a portion of which appears below, he tackles the ethical considerations of using brain organoids in research.
‘Neuroethics: The Implications of Mapping and Changing the Brain,’ an excerpt
In his new book, published today, philosopher Walter Glannon examines the ethics of six areas of neuroscience. In Chapter 4, a portion of which appears below, he tackles the ethical considerations of using brain organoids in research.