Computational neuroscience
Recent articles
Explaining ‘the largest unexplained number in brain science’: Q&A with Markus Meister and Jieyu Zheng
The human brain takes in sensory information roughly 100 million times faster than it can respond. Neuroscientists need to explore this perceptual paradox to better understand the limits of the brain, Meister and Zheng say.
Explaining ‘the largest unexplained number in brain science’: Q&A with Markus Meister and Jieyu Zheng
The human brain takes in sensory information roughly 100 million times faster than it can respond. Neuroscientists need to explore this perceptual paradox to better understand the limits of the brain, Meister and Zheng say.
What are recurrent networks doing in the brain?
The cortex is filled with excitatory local synapses, but we know little about their role in brain function. New experimental tools, along with ideas from artificial intelligence, are poised to change that.
What are recurrent networks doing in the brain?
The cortex is filled with excitatory local synapses, but we know little about their role in brain function. New experimental tools, along with ideas from artificial intelligence, are poised to change that.
Imagining the ultimate systems neuroscience paper
A growing body of papers on systems neuroscience and on giant simulations of neural circuits involves data beyond the point that anyone can reasonably understand end to end. Looking ahead, “paper-bots” could solve that problem.
Imagining the ultimate systems neuroscience paper
A growing body of papers on systems neuroscience and on giant simulations of neural circuits involves data beyond the point that anyone can reasonably understand end to end. Looking ahead, “paper-bots” could solve that problem.
NeuroAI: A field born from the symbiosis between neuroscience, AI
As the history of this nascent discipline reveals, neuroscience has inspired advances in artificial intelligence, and AI has provided a testing ground for models in neuroscience, accelerating progress in both fields.
NeuroAI: A field born from the symbiosis between neuroscience, AI
As the history of this nascent discipline reveals, neuroscience has inspired advances in artificial intelligence, and AI has provided a testing ground for models in neuroscience, accelerating progress in both fields.
What the brain can teach artificial neural networks
The brain offers valuable lessons to artificial neural networks to boost their data and energy efficiency, flexibility and more.
What the brain can teach artificial neural networks
The brain offers valuable lessons to artificial neural networks to boost their data and energy efficiency, flexibility and more.
Widely distributed brain areas sync to orchestrate decisions in rodents
Multiple brain areas synchronize their activity to help a rodent accumulate the evidence it needs to make a choice, two new studies suggest.
Widely distributed brain areas sync to orchestrate decisions in rodents
Multiple brain areas synchronize their activity to help a rodent accumulate the evidence it needs to make a choice, two new studies suggest.
Cristina Savin and Tim Vogels discuss how AI has shaped their neuroscience research
Not all neuroscientists use artificial intelligence in the same way or for the same purpose. Neuroscience researchers from different fields discuss the impact AI has had on their research and how it influences productivity in their labs.
Cristina Savin and Tim Vogels discuss how AI has shaped their neuroscience research
Not all neuroscientists use artificial intelligence in the same way or for the same purpose. Neuroscience researchers from different fields discuss the impact AI has had on their research and how it influences productivity in their labs.
How neuroscience comics add KA-POW! to the field: Q&A with Kanaka Rajan
The artistic approach can help explain complex ideas frame by frame without diluting the science, Rajan says.
How neuroscience comics add KA-POW! to the field: Q&A with Kanaka Rajan
The artistic approach can help explain complex ideas frame by frame without diluting the science, Rajan says.
Kenneth Harris and Andreas Tolias explain how AI has informed their neuroscience research
Modern AI models have shaped how the pair thinks about our brains and minds, asks research questions and views scientific progress and productivity.
Kenneth Harris and Andreas Tolias explain how AI has informed their neuroscience research
Modern AI models have shaped how the pair thinks about our brains and minds, asks research questions and views scientific progress and productivity.
Neural manifolds: Latest buzzword or pathway to understand the brain?
When you cut away the misconceptions, neural manifolds present a conceptually appropriate level at which systems neuroscientists can study the brain.
Neural manifolds: Latest buzzword or pathway to understand the brain?
When you cut away the misconceptions, neural manifolds present a conceptually appropriate level at which systems neuroscientists can study the brain.
Explore more from The Transmitter
The non-model organism “renaissance” has arrived
Meet 10 neuroscientists bringing model diversity back with the funky animals they study.
The non-model organism “renaissance” has arrived
Meet 10 neuroscientists bringing model diversity back with the funky animals they study.
Assembloids illuminate circuit-level changes linked to autism, neurodevelopment
These complex combinations of organoids afford a closer look at how gene alterations affect certain brain networks.
Assembloids illuminate circuit-level changes linked to autism, neurodevelopment
These complex combinations of organoids afford a closer look at how gene alterations affect certain brain networks.
Rajesh Rao reflects on predictive brains, neural interfaces and the future of human intelligence
Twenty-five years ago, Rajesh Rao proposed a seminal theory of how brains could implement predictive coding for perception. His modern version zeroes in on actions.
Rajesh Rao reflects on predictive brains, neural interfaces and the future of human intelligence
Twenty-five years ago, Rajesh Rao proposed a seminal theory of how brains could implement predictive coding for perception. His modern version zeroes in on actions.