Bayesian Efficient Coding

On 15 sep 2017, we discussed Bayesian Efficient Coding by Il Memming Park and Jonathan Pillow.

As the title suggests, the authors aim to synthesize bayesian inference with efficient coding. The Bayesian brain hypothesis states that the brain computes posterior probabilities based on its model of the world (prior) and its sensory measurements (likelihood). Efficient coding assumes that the brain distributes its resources to maximize a cost, typically information. In particular, they note that efficient coding that optimizes mutual information is a special case of their more general framework, but ask whether other maximizations based on the Bayesian posterior might better explain data.

Denoting stimulus x, measurements y, and model parameters \theta, they use the following ingredients for their theory: a prior p(x), a likelihood p(y|x), an encoding capacity constraint C(\theta), and a loss functional L(\cdot). They assume that the brain is able to construct the true posterior p(x|y,\theta). The goal is to find a model that optimizes the expected loss


under the constraint C(\theta)\leq c.

The loss functional is the key. The authors consider two things the loss might depend on: the posterior L(p(x|y)), or the ground truth L(x,p(x|y)). They needed to make the loss explicitly dependent on the posterior in order to optimize for mutual information. It was unclear whether they also considered a loss depending on both, which seems critical. We communicated with them and they said they’d clarify this in the next version.

They state that there is no clear a priori reason to maximize mutual information (or equivalently to minimize the average posterior entropy, since the prior is fixed). They give a nice example of a multiple choice test for which encodings that maximize information will achieve fewer correct answers than encodings that maximize percent correct for the MAP estimates. The ‘best’ answer depends on how one defines ‘best’.

After another few interesting gaussian examples, they revisit the famous Laughlin (1981) result on efficient coding in the blowfly. This was hailed as a triumph for efficient coding theory in predicting the nonlinear input-output photoreceptor curve derived directly from the measured prior over luminance. But here the authors found that instead a different loss function on the posterior gave a better fit. Interestingly, though, that loss function was based on a point estimate,


where the point estimate is the Bayesian optimum for this cost function and p is a parameter. The limit p\to 0 gives the familiar entropy, p=2 is the conventional squared error, and the best fit to the data was p=1/2, a “square root loss.” It’s hard to provide any normative explanation of why this or any other choice is best (since the loss is basically the definition of ‘best’, and you’d have to relate the theoretical loss to some real consequences in the world), it is very interesting that the efficient coding solution explains data worse than their other Bayesian efficient coding losses.

Besides the minor confusion about whether their loss does/should include the ground truth x, and some minor disagreement about how much others have done things along this line (Ganguli and Simoncelli, Wei and Stocker, whom they do cite), my biggest question is whether the cost really should depend on the posterior as opposed to a point estimate. I’m a fan of Bayesianism, but ultimately one must take a single action, not a distribution. I discussed this with Jonathan over email, and he maintained that it’s important to distinguish an action from a point estimate of the stimulus: there’s a difference between the width of the river and whether to try to jump over it. I countered that one could refer actions back to the stimulus: the river is jumpable, or unjumpable (essentially a Gibsonian affordance). In a world of latent variables, any point estimate based on a posterior is a compromise based on the loss function.

So when should you keep around a posterior, rather than a point estimate? It may be that the appropriate loss function changes with context, and so the best point estimate would change too. While one could certainly consider that to be a bigger computation to produce a context-dependent point estimate, it may be more parsimonious to just represent information about the posterior directly.

NeuroTheory Journal Club blog


We are a cross-departmental student-run group, whose aim is to bring together the Houston computational neuroscience community (BCM/RICE/UH/UTHealth). We meet weekly to discuss papers. Every other week will be focused on our NeuroNex center project to infer graphical models for interactions between neurons and the world. Other weeks we will cover general topics in computational neuroscience, including cellular, systems, cognitive, stats, machine learning topics.

Meeting Time & Place: Friday @ 9:00am-10:00am, in BCM room S553.

Contact: KiJung [dot] Yoon [at] bcm [dot] edu