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.

Animals have evolved to rapidly detect and recognise brief and intermittent encounters with odour packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results -- existing solutions are either slow; or bulky, expensive, and power-intensive -- limiting applicability in real-world scenarios for mobile robotics. Here we introduce a miniaturised high-speed electronic nose; characterised by high-bandwidth sensor readouts, tightly controlled sensing parameters and powerful algorithms. The system is evaluated on a high-fidelity odour delivery benchmark. We showcase successful classification of tens-of-millisecond odour pulses, and demonstrate temporal pattern encoding of stimuli switching with up to 60 Hz. Those timescales are unprecedented in miniaturised low-power settings, and demonstrably exceed the performance observed in mice. For the first time, it is possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

"Wait a minute, wait a minute. You ain't heard nothing yet." So went the first line of audible dialogue in a feature film, 1927's The Jazz Singer. It was one of the first times that mass media had conveyed the sight and sound of a scene together, and the audience was enthralled. There have been improvements since: black and white has become colour, frame rates and resolutions have increased and sound quality has improved, but the media we consume still caters overwhelmingly, if not exclusively, to our eyes and ears.

Did you ever try to measure a smell?

Neuromorphic computing is one of the few current approaches that have the potential to significantly reduce power consumption in Machine Learning and Artificial Intelligence. Imam & Cleland presented an odour-learning algorithm that runs on a neuromorphic architecture and is inspired by circuits described in the mammalian olfactory bulb. They assess the algorithm's performance in "rapid online learning and identification" of gaseous odorants and odorless gases (short "gases") using a set of gas sensor recordings of different odour presentations and corrupting them by impulse noise. We replicated parts of the study and discovered limitations that affect some of the conclusions drawn. First, the dataset used suffers from sensor drift and a non-randomised measurement protocol, rendering it of limited use for odour identification benchmarks. Second, we found that the model is restricted in its ability to generalise over repeated presentations of the same gas. We demonstrate that the task the study refers to can be solved with a simple hash table approach, matching or exceeding the reported results in accuracy and runtime. Therefore, a validation of the model that goes beyond restoring a learned data sample remains to be shown, in particular its suitability to odour identification tasks.

Researchers from Tel Aviv University have created a robot that can smell and identify odours using a biological sensor. The researchers connected the sensor to an electronic system. They used a machine learning algorithm to detect odours with a level of sensitivity that is 10,000 times higher than that of a commonly used electronic device. The sensor sends electrical signals as a response to the presence of a nearby odour, which the robot can detect and interpret. According to the University, the researchers say, "The sky's the limit," and believe this technology may also be used to identify explosives, drugs, diseases, and more.

Google has used an artificial intelligence to produce a map that relates smells to the structures of molecules. It is as reliable as a human in describing the odour of a substance and the researchers behind the work say it is a crucial step towards digitising odours.

Computer scientists at Google have developed an artificial intelligence (AI) tool that can describe what something will smell like by its chemical structure. It uses an'odour map' to visualise the indicative scents of a particular molecule, building on work from 2019 where the technology described scents using words. Points that represent similar odours appear close together on the map, which can be used to predict what a substance will smell like before humans have a sniff. 'The model is as reliable as a human in describing odor quality,' the researchers from Cambridge, Massachusetts, USA wrote. They hope that the AI model could be used to identify new scents for fragrances, or flavour profiles in food formulation.

Google has used an artificial intelligence to produce a map that relates smells to the structures of molecules. It is as reliable as a human in describing the odour of a substance and the researchers behind the work say it is a crucial step towards digitising odours.

Scientists have taught mice to smell an odour that doesn't exist in order in a study to show how the brain identifies different scents. In experiments on mice, US neuroscientists generated an electrical signature that was perceived as an odour in the brain's smell-processing centre, the olfactory bulb. Because the odour-simulating signal was handmade, researchers could manipulate the timing and order of related nerve signalling like'musical notes'. From this, they could identify which changes were most important to the ability of mice to accurately identify the'synthetic smell'. The team claims to have decoded how mammalian brains perceive odours and distinguish one smell from thousands of others.