optimal coding | Pillow Lab Blog

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):