This week, Ozan presented a recent paper from Matthias Bethge’s group:
A. S. Ecker, P. Berens, A. S. Tolias, and M. Bethge
The effect of noise correlations in populations of diversely tuned neurons
The Journal of Neuroscience, 2011
The paper describes an analysis of the effects of correlations on the coding properties of a neural population, analyzed using Fisher information. The setup is that of a 1D circular stimulus variable (e.g., orientation) encoded by a population of N neurons defined by a bank of tuning curves (specifying the mean of each neuron’s response), and a covariance matrix describing the correlation structure of additive “proportional” Gaussian noise.
The authors find that when the tuning curves are heterogeneous (i.e., not shifted copies of a single Gaussian bump), then noise correlations do not reduce Fisher information. So correlated noise is not necessarily harmful. This seems surprising in light of a bevy of recent papers showing that the primary neural correlate of perceptual improvement (due to learning, attention, etc.) is a reduction in noise correlations. (So Matthias, what is going on??).
It’s a very well written paper, very thorough, with gorgeous figures. And I think it sets a new record for “most equations in the main body of a J. Neuroscience paper”, at least as far as I’ve ever seen. Nice job, guys!
Pillow Lab Blog 