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
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.
COVID-19 and influenza effects in pregnancy; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 October.
COVID-19 and influenza effects in pregnancy; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 October.
What to do when traditional mechanistic approaches no longer work
Many complex behaviors have stubbornly resisted mechanistic explanations. Deciphering them will require new approaches—and perhaps a new concept of “understanding.”
What to do when traditional mechanistic approaches no longer work
Many complex behaviors have stubbornly resisted mechanistic explanations. Deciphering them will require new approaches—and perhaps a new concept of “understanding.”