Headshot of Kenneth Harris.

Kenneth Harris

Professor of quantitative neuroscience
University College London

Kenneth Harris is professor of quantitative neuroscience at University College London. Together with Matteo Carandini, he co-directs the Cortical Processing Laboratory. The aim of the laboratory is to understand the computations performed by neuronal populations in the visual system, the underlying neural circuits and the way these computations lead to perceptual decisions. Current research efforts focus on how cortical populations integrate sensory information with information from within the brain.

Harris received a B.A. and Part III in mathematics from the University of Cambridge in 1993 and a Ph.D. in neural computation from University College London in 1998. He then moved to Rutgers University for postdoctoral work, where he eventually opened a laboratory studying neuronal population activity in the neocortex. He next moved to Imperial College London before joining the faculty at University College London.

Harris received the Alfred P. Sloan Fellowship in 2005 and the Royal Society Wolfson Research Merit Award and an EPSRC Leadership Fellowship in 2010. He was named a Burroughs Wellcome Trust Investigator and a Simons Investigator in 2014.

Explore more from The Transmitter

Clockwise: lab mouse, zebrafish, octopus, rat pushing a cube, nematodes, fruit fly.

The term ‘model organism’ should be reserved for biological systems

By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.

By Marc Coutanche
13 November 2026 | 6 min read

New phase of BRAIN Initiative spawns basal ganglia ‘parts list’

With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.

By Claudia López Lloreda
12 November 2026 | 1 min watch
Research image showing genes containing microexons in mice.

Machine-learning model spots autism-linked variants that scramble splicing

The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.

By Giorgia Guglielmi
12 November 2026 | 4 min read