Neural Circuit Dynamics for Sensory Detection

Abstract

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns, i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment. We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high fidelity tracking of a nominal representation in this latent space in an energy efficient manner. It turns out that the optimal motifs emerging from this framework possess morphological similarity with prototypical onset and offset responses observed in vivo. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory-inhibitory competitive dynamics, similar to interactions between principal neurons (PNs) and local neurons (LNs) in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and tradeoffs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection. Significance StatementA key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we treat not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. We examine through optimization-based synthesis how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.