🤖 AI Summary
In neural perception, conventional probabilistic inference requiring direct connections between latent units becomes biologically implausible and computationally prohibitive when latent variables exhibit genuine statistical dependencies. To address this, we propose a novel neural circuit mechanism inspired by “sister cells”: pairs of neurons sharing common input but exhibiting divergent local connectivity, thereby indirectly encoding prior correlations among latent variables without direct inter-latent connections. We introduce a geometric construction method for synaptic connectivity that formally establishes the invertibility of prior structure from circuit architecture. Integrating geometric modeling, dynamical circuit simulation, and probabilistic inference theory, we validate the mechanism within an olfaction-inspired architecture. Results demonstrate significantly improved inference accuracy under noise, dynamical behaviors consistent with experimental observations, and—under biologically reasonable assumptions—the unique reconstruction of the latent prior structure from sister-cell activity alone.
📝 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.