Invariant Odor Recognition with ON-OFF Neural Ensembles

Abstract

Invariant recognition of a stimulus is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus could be perturbed in a multitude of ways, could a single scheme be devised to achieve this computational capability? We examined this issue in locust olfactory system. We found that odor-evoked responses in individual projection neurons in the locust antennal lobe varied unpredictably with repetition, stimulus dynamics, stimulus history, presence of background odorants, and changes in ambient conditions. Yet, a highly-constrained Bayesian logistic regression approach with ternary weights could provide robust odor recognition. We found that this approach could be further simplified: sum firing rates of ON neurons and subtract total activity in OFF neurons (‘ON minus OFF’ classifier). Notably, we found that this approach could be generalized to develop a Boolean neural network that can perform well in a non-olfactory pattern recognition task.

Invariant odor recognition with ON–OFF neural ensembles

“…Data Availability. All data presented in this paper are publicly available in Figshare (DOI: 10.6084/m9.figshare.17162570) (22). Previously published data were used for this work (some behavioral data used in Figs.…”

Neural manifolds for odor-driven innate and acquired appetitive preferences

“…Earlier studies have indeed shown that individual projection neurons responses change unpredictably with changes in stimulus dynamics, intensity, competing cues, stimulus history, and ambient conditions 16,22,23,39,40 . Notably, the behavioral recognition of odorants was found to remain invariant under a battery of these perturbations 41 . Therefore, a more robust and fault-tolerant model to overcome such variations in neural responses that arise due to natural perturbations would involve a combinatorial read-out of the ensemble activity as proposed in our regression analyses.…”