Computational neuroscience
Recent articles
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?
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.
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.
‘The Brain, In Theory,’ an excerpt
In his new book, Brette pushes back against theories that describe the brain as a “biological computer.” In this excerpt from Chapter 4, he challenges equating brain evolution with programming, and the universality of neural network models.
‘The Brain, In Theory,’ an excerpt
In his new book, Brette pushes back against theories that describe the brain as a “biological computer.” In this excerpt from Chapter 4, he challenges equating brain evolution with programming, and the universality of neural network models.
Computational neuroscientist Keith Hengen explains his work through illustrations
The images help him communicate the “big-picture ideas” behind the mathematical principles of neuronal networks.
Computational neuroscientist Keith Hengen explains his work through illustrations
The images help him communicate the “big-picture ideas” behind the mathematical principles of neuronal networks.
Explore more from The Transmitter
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.
Arboreal deer mice reveal neural roots of dexterity
The rodents offered researchers an opportunity to link genetically driven changes in corticospinal abundance and morphology to climbing cachet.
Arboreal deer mice reveal neural roots of dexterity
The rodents offered researchers an opportunity to link genetically driven changes in corticospinal abundance and morphology to climbing cachet.
Single-gene systems-level effects, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 6 April.
Single-gene systems-level effects, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 6 April.