Noise

stands for

n

eural m

o

tifs,

i

nternal

s

tates, and

e

volution. 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 a powerful mechanism for discovery and learning.

We're interested in

how how goals, beliefs, expectations, and even arousal shape how we see and interact with the world. We study (1) how these internal states change the way we transform sensation into action, and (2) how we adjust our internal states in order to achieve different goals.

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 electrophysiology, 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.