Artificial neural networks
Recent articles
This paper changed my life: Appreciating John Hopfield’s brilliant neural network
In a 1982 paper, the Nobel laureate created his namesake recurrent neural network—work that taught Maria Geffen to always ground research questions in biology.
This paper changed my life: Appreciating John Hopfield’s brilliant neural network
In a 1982 paper, the Nobel laureate created his namesake recurrent neural network—work that taught Maria Geffen to always ground research questions in biology.
Kim Stachenfeld on the dance between neuroscience and artificial intelligence
As a researcher at both Google DeepMind and Columbia University, Stachenfeld offers cross-disciplinary insight into how to understand the brain.
Kim Stachenfeld on the dance between neuroscience and artificial intelligence
As a researcher at both Google DeepMind and Columbia University, Stachenfeld offers cross-disciplinary insight into how to understand the brain.
Explore more from The Transmitter
Is it time for neuroscience to shift its focus from a computational mind to a navigating mind?
Andrea Hiott explains her navigational approach to the mind, and why now is the time to embrace seemingly paradoxical findings in neuroscience.
Is it time for neuroscience to shift its focus from a computational mind to a navigating mind?
Andrea Hiott explains her navigational approach to the mind, and why now is the time to embrace seemingly paradoxical findings in neuroscience.
Human brain organoids come of age—but still have room to grow
The U.S. government has touted organoids as an alternative to animals for studying the brain. The model has improved since its debut in 2013, but scientists are still working to overcome key limitations: immaturity, consistency and the lack of vascularization.
Human brain organoids come of age—but still have room to grow
The U.S. government has touted organoids as an alternative to animals for studying the brain. The model has improved since its debut in 2013, but scientists are still working to overcome key limitations: immaturity, consistency and the lack of vascularization.
Finding neuroheterogeneity’s sweet spot
Once thought to be detrimental to memory processing, neuronal diversity—at the right ‘Goldilocks’ dose—is a new boon to theoretical neuroscience.
Finding neuroheterogeneity’s sweet spot
Once thought to be detrimental to memory processing, neuronal diversity—at the right ‘Goldilocks’ dose—is a new boon to theoretical neuroscience.