Noise stands for neural motifs, internal states, and evolution. This backronym describes our goal (discovering the neural motifs underlying cognition), our approach (studying variability across internal states), and one of our philosophical commitments (that the brain is the product of evolution and must be understood in its ecological context). Noise itself is just one powerful mechanism for discovery and learning, a problem we spend a lot of time thinking about.
We're interested in how the brain's internal states shape how we see, interact with, and learn about the world. Specifically, we study (1) how goals, beliefs, expectations, and even arousal change the way that neural populations transform sensation into action, and (2) how we adjust these states as we learn.
Our work shows that we can generate the same sensorimotor transformation in very different ways, depending on why we're doing it (e.g. 1, 2).
Methodologically, we combine large-scale neural recordings, causal perturbations, and psychophysics with computational models. We like to identify internal states from first principles: through characterizing the latent structure in behavior. We enthusiastically incorporate tools, approaches, and insight from other disciplines, especially ethology and artificial intelligence.
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