Inference with correlated priors using sisters cells

TL;DR

This paper introduces a biologically inspired neural circuit motif using sister cells to enable inference with correlated priors without direct interactions between latent cause units, improving sensory inference models.

Contribution

The authors propose a novel circuit architecture inspired by the olfactory bulb that allows neural inference with correlated priors through indirect interactions via sister cells.

Findings

Effective inference with correlated priors demonstrated in simulations.

Connectivity design bounds for constructing priors based on geometric arguments.

Prior inference from sister cell activations under certain assumptions.

Abstract

A common view of sensory processing is as probabilistic inference of latent causes from receptor activations. Standard approaches often assume these causes are a priori independent, yet real-world generative factors are typically correlated. Representing such structured priors in neural systems poses architectural challenges, particularly when direct interactions between units representing latent causes are biologically implausible or computationally expensive. Inspired by the architecture of the olfactory bulb, we propose a novel circuit motif that enables inference with correlated priors without requiring direct interactions among latent cause units. The key insight lies in using sister cells: neurons receiving shared receptor input but connected differently to local interneurons. The required interactions among latent units are implemented indirectly through their connections to the sister cells, such that correlated connectivity implies anti-correlation in the prior and vice versa. We use geometric arguments to construct connectivity that implements a given prior and to bound the number of causes for which such priors can be constructed. Using simulations, we demonstrate the efficacy of such priors for inference in noisy environments and compare the inference dynamics to those experimentally observed. Finally, we show how, under certain assumptions on latent representations, the prior used can be inferred from sister cell activations. While biologically grounded in the olfactory system, our mechanism generalises to other natural and artificial sensory systems and may inform the design of architectures for efficient inference under correlated latent structure.