Nobel Prizes in Neuroscience: A Timeline

From Golgi and Cajal in 1906 to the receptors for heat and touch in 2021 — the Nobel Prizes that mark the major discoveries about the nervous system.

The Nobel Prize in Physiology or Medicine has been awarded since 1901. A large share of its prizes have gone to work on the nervous system. Here is a selection.

Seeing and signalling (1906–1963)

1906 — Camillo Golgi and Santiago Ramón y Cajal, for the structure of the nervous system. 1932 — Charles Sherrington and Edgar Adrian, for the functions of neurons. 1936 — Henry Dale and Otto Loewi, for chemical transmission. 1944 — Joseph Erlanger and Herbert Gasser, for the different functions of single nerve fibres. 1949 — Walter Hess, for the brain's control of internal organs; shared that year with Egas Moniz for the lobotomy, now regarded as one of the most regrettable awards in the prize's history. 1963 — John Eccles, Alan Hodgkin and Andrew Huxley, for the ionic basis of nerve signalling.

Transmitters, senses and development (1970–1997)

1970 — Bernard Katz, Ulf von Euler and Julius Axelrod, for how transmitters are stored, released and inactivated. 1981 — Roger Sperry (split-brain research), David Hubel and Torsten Wiesel (visual processing). 1986 — Rita Levi-Montalcini and Stanley Cohen, for growth factors. 1991 — Erwin Neher and Bert Sakmann, for single ion channels (the patch clamp). 1997 — Stanley Prusiner, for prions.

Memory, senses and maps (2000–2021)

2000 — Arvid Carlsson, Paul Greengard and Eric Kandel, for signal transduction in the nervous system. 2004 — Richard Axel and Linda Buck, for odorant receptors. 2014 — John O'Keefe, May-Britt Moser and Edvard Moser, for the brain's positioning system. 2017 — Jeffrey Hall, Michael Rosbash and Michael Young, for the molecular mechanisms of circadian rhythm. 2021 — David Julius and Ardem Patapoutian, for receptors for temperature and touch.

A physics prize, too

In 2024 the Nobel Prize in Physics went to John Hopfield and Geoffrey Hinton for foundational work on artificial neural networks — ideas inspired directly by how real neurons might store and process information.