A Computational Cognitive and Visual Neuroscience Laboratory
PI: David J. Freedman, Ph.D.
Through experience, we learn to interpret the sights and sounds around us and to make decisions that move us closer to achieving our goals. Our ability to learn from and adapt to our ever changing environment is a foundation of complex behavior, as it allows us to make sense of incoming sensory stimuli and to plan successful actions. To study these questions, our laboratory uses advanced neurophysiological and behavioral techniques, in parallel with machine learning approaches for studying cognitive computations in artificial neural networks. Together, our work is providing insights into the brain mechanisms of visual learning, recognition and decision making.Learn more
A collaborative paper with the Maunsell lab shows that perceptual decisions are preferentially driven by increases rather than decreases in neuronal spiking
Big congrats to FreedmanLab grad student Jeff Johnston on his new theory paper on nonlinear mixed selectivity in PLoS Computational Biology. In collaboration with Stephanie Palmer.
Check out our new review article in TICS from Nick Masse and Matt Rosen on mechanisms of short-term working memory.
Congratulations to FreedmanLab Alum Krithika Mohan on her postdoc position at Caltech!
Our latest paper is in press at Science! Postdoc Yang Zhou shows that posterior parietal cortex plays a causal role in perceptual and categorical decisions.
Nick Masse’s paper on mechanisms of activity-silent working memory published in Nature Neuroscience!
Our new paper from Nick Masse and Greg Grant in press at PNAS, revealing a novel strategy for alleviating catastrophic forgetting in artificial neural networks.
FreedmanLab alum Guilhem Ibos named CNRS Faculty at Institut de Neurosciences de la Timone
David Freedman named a Vannevar Bush Faculty Fellow by the USA Department of Defense