Abstract
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bulb, but our approach is general enough to apply to other modalities. In addition, it should be possible to extend it to nonlinear encoding models.
Highlights
A common view of sensory systems is that they invert generative models of the environment to infer the causes underlying sensory input
In this work we take inspiration from the ‘sister’ mitral cells of the olfactory system—groups of neurons associated with the same input channel—to derive a method for performing MAP inference using sparse connectivity
We do so by assigning sister cells to random subsets of the latent variables and using additional cells to ensure that sisters correctly share information
Summary
A common view of sensory systems is that they invert generative models of the environment to infer the causes underlying sensory input. Sensory input is typically ambiguous, so a given input can be explained by multiple causes. Correct inference requires adequately accounting for interactions among causes. Any neural circuit performing inference must implement mechanisms for inter-causal interaction. This typically results in dense—and in many cases all-to-all—connectivity between neurons representing causes. The myriad causes potentially responsible for a given sensory input often require a neuron representing a cause to connect to hundreds of thousands of others.
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