Lab Meeting 2/4/2013: Asymptotically optimal tuning curve in Lp sense for a Poisson neuron

Optimal tuning curve is the best transformation of the stimulus into neural firing pattern (usually firing rate) under certain constraints and optimality criterion. The following paper I saw at NIPS 2012 was related to what we are doing, so we took a deeper look into it.

Wang, Stocker & Lee (NIPS 2012), Optimal neural tuning curves for arbitrary stimulus distributions: Discrimax, infomax and minimum Lp loss.

The paper assumes a single neuron encoding a 1 dimensional stimulus, governed by a distribution \pi(s). The neuron is assumed to be Poisson (pure rate code). The neuron’s tuning curve h(s) is smooth, monotonically increasing (with h'(s) > c), and has a limited minimum and maximum firing rate as its constraint. Authors assume asymptotic regime for MLE decoding where the observation time T is long enough to apply asymptotic normality theory (and convergence of p-th moments) of MLE.

The authors show that there is a 1-to-1 mapping between the tuning curve and the Fisher information I under these constraints. Then for various loss functions, they derive the optimal tuning curve using calculus of variations. In general, to minimize the Lp loss E\left[ |\hat s - s|^p \right] under the constraints, the optimal (squared) tuning curve is:

\sqrt(h(s)) = \sqrt{h_{min}} + (\sqrt{h_{max}} - \sqrt{h_{min}}) \frac{\int_{-\infty}^s \pi(t)^{1/(p+1)} \mathrm{d}t}{\int_{-\infty}^\infty \pi(t)^{1/(p+1)} \mathrm{d}t}

Furthermore, in the limit of p \to 0, the optimal solution corresponds to the infomax solution (i.e., optimum for mutual information loss). However, all the analysis is only in the asymptotic limit, where the Cramer-Rao bound is attained by the MLE. For the case of mutual information, unlike noise-less case where the optimal tuning curve becomes the stimulus CDF (Laughlin), for Poisson noise, it turns out to be the square of the stimulus CDF. I have plotted the differences below for a normal distribution (left) and a mixture of normals (right):

Comparison of optimal tuning curves

The results are very nice, and I’d like to see more results with stimulus noise and with population tuning assumptions.

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