Pitkow et al., *Neuron*, 87, 411-423, 2015

A couple of weeks ago I presented Xaq Pitkow et al.’s paper examining the convoluted relationship between choice probabilities (CP), information-limiting correlations (ILC), and suboptimal coding.

4

Pitkow et al., *Neuron*, 87, 411-423, 2015

A couple of weeks ago I presented Xaq Pitkow et al.’s paper examining the convoluted relationship between choice probabilities (CP), information-limiting correlations (ILC), and suboptimal coding.

Advertisements

I’m proud to announce the publication of our “zombie” spike sorting paper (Pillow, Shlens, Chichilnisky & Simoncelli 2013), which addresses the problem of detecting overlapped spikes in multi-electrode recordings.

The basic problem we tried to address is that standard “clustering” based spike-sorting methods often miss near-synchronous spikes. As a result, you get cross-correlograms that look like this:

When I first saw these correlograms (back in 2005 or so), I thought: “Wow, amazing —retinal ganglion cells inhibit each other with 1-millisecond precision! Should we send this to Nature or Science?” My more sober experimental colleagues pointed out that that this was likely only a (lowly) spike sorting artifact. So we set out to address the problem (leading to the publication of this paper a mere 8 years later!)

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!