Machine learning
Recent articles
What are the fastest-growing areas in neuroscience?
Respondents pointed to computational neuroscience, systems neuroscience, neuroimmunology and neuroimaging, among other subfields.
What are the fastest-growing areas in neuroscience?
Respondents pointed to computational neuroscience, systems neuroscience, neuroimmunology and neuroimaging, among other subfields.
What are the most transformative neuroscience tools and technologies developed in the past five years?
Artificial intelligence and deep-learning methods featured prominently in the survey responses, followed by genetic tools to control circuits, advanced neuroimaging, transcriptomics and various approaches to record brain activity and behavior.
What are the most transformative neuroscience tools and technologies developed in the past five years?
Artificial intelligence and deep-learning methods featured prominently in the survey responses, followed by genetic tools to control circuits, advanced neuroimaging, transcriptomics and various approaches to record brain activity and behavior.
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.
Explore more from The Transmitter
Autism scientists push back on CDC’s inaccurate vaccine claims
The CDC website now falsely suggests that autism-vaccine research is still an open question, prompting distrust among researchers—some of whom anticipate “more unreliable statements coming from the junta that took over” the agency.
Autism scientists push back on CDC’s inaccurate vaccine claims
The CDC website now falsely suggests that autism-vaccine research is still an open question, prompting distrust among researchers—some of whom anticipate “more unreliable statements coming from the junta that took over” the agency.
Gene replacement therapy normalizes some traits in SYNGAP1 model mice
The first published virus-based gene therapy for SYNGAP1 deletion yields benefits despite the gene’s long length and complexity.
Gene replacement therapy normalizes some traits in SYNGAP1 model mice
The first published virus-based gene therapy for SYNGAP1 deletion yields benefits despite the gene’s long length and complexity.
Does AI understand what it produces? Henk de Regt explores how we might assess understanding in machines and humans
Building on his philosophy of how scientists understand what they work on, de Regt is extending his approach to test understanding in machines.
Does AI understand what it produces? Henk de Regt explores how we might assess understanding in machines and humans
Building on his philosophy of how scientists understand what they work on, de Regt is extending his approach to test understanding in machines.