First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This study investigates two algorithms for fast inference in generative models of olfaction. Their goal is to compute the most likely, linear mixture of odors comprising an olfactory stimulus. One of the algorithms employs variational inference, while the other is based on a sampling scheme. Simulations demonstrate that both algorithms perform suitably well, and the authors claim that inference is performed rapidly within the first 100 ms, while eliminating false positives (detection of odors not present in a particular stimulus) takes much longer and is difficult when more than two odors are present.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper attempts to link sparse optimization methodology to the anatomical structure of locust's early olfactory system. The work is motivated by the observation that odorant molecules are sparsely represented by the population of Kenyon cells. The authors first mathematically formulate the olfactory system as a MAP decoder, and give the standard solution to the problem without considering biological constraints. Next, to make the solution more biologically plausible, the authors reformulate the olfactory system model as a decoder of a compressive sensing problem, and provide two standard solutions to the dual problem. Then, the authors argue that each of the components in the solution can be mapped/interpreted to/as a unit of the biological structure in the olfactory system. However, these maps are described without a strong justification and there are conceptual problems in linking the math with the biology.

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.

The murky ink from cuttlefish (Sepia officinalis) could help deter sharks from hunting near where people are swimming. After a team at University College Dublin created models of the olfactory systems of several species of sharks, they found that the bioluminescent cephalopod's ink might overwhelm the sharks' heightened sensitivity to odors. The findings are detailed in a study recently published in the journal G3: Genes, Genomes, Genetics. "Understanding how prey species like cuttlefish have evolved to exploit specific vulnerabilities in predators like sharks enriches not only our understanding of marine ecosystems but provides inspiration for conservation tools rooted in natural processes," study co-author and biologist Colleen Lawless said in a statement. By signing up you agree to our Terms of Service and Privacy Policy.

We study the early locust olfactory system in an attempt to explain its wellcharacterized structure and dynamics. We first propose its computational function as recovery of high-dimensional sparse olfactory signals from a small number of measurements.

The olfactory system employs responses of an ensemble of odorant receptors (ORs) to sense molecules and to generate olfactory percepts. Here we hypothesized that ORs can be viewed as 3D spatial filters that extract molecular features relevant to the olfactory system, similarly to the spatio-temporal filters found in other sensory modalities. To build these filters, we trained a convolutional neural network (CNN) to predict human olfactory percepts obtained from several semantic datasets. Our neural network, the DeepNose, produced responses that are approximately invariant to the molecules' orientation, due to its equivariant architecture. Our network offers high-fidelity perceptual predictions for different olfactory datasets. In addition, our approach allows us to identify molecular features that contribute to specific perceptual descriptors. Because the DeepNose network is designed to be aligned with the biological system, our approach predicts distinct perceptual qualities for different stereoisomers. The architecture of the DeepNose relying on the processing of several molecules at the same time permits inferring the perceptual quality of odor mixtures. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts and help identify molecular features responsible for odor quality.

Brains are bewilderingly complicated systems of connections between neurons. Mapping those connections is an important step in understanding how brains work. Scientists have recently completed the most ambitious effort yet to construct such a map: a complete document of every neuron and every connection in the brain of an adult fruit fly. The research represents the first such map for an animal that can walk and see, and the first complete map of the brain of an adult animal. It traces each and every one of the 139,255 neurons in the brain of Drosophila melanogaster, along with the 50 million connections between them, and is by far the largest and most detailed ever produced.

Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.

Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging.

We study the early locust olfactory system in an attempt to explain its wellcharacterized structure and dynamics. We first propose its computational function as recovery of high-dimensional sparse olfactory signals from a small number of measurements.