Peter Hegemann, Georg Nagel and Karl Deisseroth win Nobel prize

Peter Hegemann, Georg Nagel and Karl Deisseroth will jointly receive the 2026 Nobel Prize in Physiology or Medicine for their discoveries concerning light-gated ion channels and optogenetics, Nobel Committee for Physiology or Medicine says. The award recognizes a method that allows researchers to demonstrate how nerve cells shape memories, feelings and behaviours in the living brain. Hegemann and Nagel first discovered the remarkable protein, channelrhodopsin, within a single-celled alga.

“Optogenetics provides opportunities for mapping the brain in a way that we could once only dream of,” says Per Svenningsson, Chair of the Nobel Committee for Physiology or Medicine, as Deisseroth then transformed this protein into a light-controlled switch for nerve cells, moving neuroscience beyond a “sketch map” of the brain.

Channelrhodopsin Discovery in Algae Enabled Optogenetics Research

The origins of optogenetics lie in an unexpected place; Peter Hegemann and Georg Nagel first identified channelrhodopsin within a single-celled alga, Chlamydomonas, while investigating how the organism navigates towards light sources. Their early 2000s research revealed that this algal protein opens a channel allowing charged ions to flow across cell membranes when exposed to blue light, effectively creating an electrical impulse regardless of the cell type.

This discovery, initially focused on algal motility, helped establish the foundation for controlling nerve cell activity. The unique properties of channelrhodopsin extended beyond simple light sensitivity; the researchers found the protein functioned consistently across different cell types, a characteristic important for its eventual application in neuroscience, according to Nobel Committee for Physiology or Medicine. Karl Deisseroth built upon this foundation by introducing the gene for channelrhodopsin into rat nerve cells.

In 2005, he demonstrated the ability to trigger nerve signals using blue light, a breakthrough that allowed for precise control of neuronal activity. Two years later, Deisseroth extended this control to the brains of living mice, establishing the core principle of optogenetics as a functional tool for manipulating neural circuits in vivo. Prior to the development of optogenetics, neuroscience relied on methods that could only create a “sketch map” of the brain, unable to definitively prove causal relationships between neural activity and behaviour.

Researchers could observe correlations, but determining whether a specific neural circuit caused a particular behaviour remained elusive. The ability to activate or inhibit specific neurons with light allows researchers to directly test hypotheses about neural circuit function, moving beyond correlation to establish causation.

The impact of optogenetics extends beyond basic research; researchers are now applying the method in clinical settings, including attempts to restore sight in individuals with visual impairment. Every day, new discoveries are being made, furthering our understanding of the brain’s complex workings and offering potential therapeutic avenues.

Optogenetics provides opportunities for mapping the brain in a way that we could once only dream of.

Per Svenningsson, Chair of the Nobel Committee for Physiology or Medicine
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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