AIMC Topic: Olfactory Perception
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Reinforced Odor Representations in the Anterior Olfactory Nucleus Can Serve as Memory Traces for Conspecifics.
eNeuro
Jul 28, 2025
Recognition of conspecific individuals in mammals is an important skill, thought to be mediated by a distributed array of neural networks, including those processing olfactory cues. Recent data from our groups have shown that social memory can be sup...
From materials to applications: a review of research on artificial olfactory memory.
Materials horizons
Mar 4, 2025
Olfactory memory forms the basis for biological perception and environmental adaptation. Advancing artificial intelligence to replicate this biological perception as artificial olfactory memory is essential. The widespread use of various robotic syst...
An overview on olfaction in the biological, analytical, computational, and machine learning fields.
Archiv der Pharmazie
Oct 22, 2024
Recently, the comprehension of odor perception has advanced, unveiling the mysteries of the molecular receptors within the nasal passages and the intricate mechanisms governing signal transmission between these receptors, the olfactory bulb, and the ...
DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection.
Communications biology
Jan 29, 2021
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate ...
A network model of affective odor perception.
PloS one
Jul 30, 2020
The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We there...
Machine-learned analysis of side-differences in odor identification performance.
Neuroscience
Nov 2, 2019
A right-left dichotomy of olfactory processes has been recognized on several levels of the perception or processing of olfactory input. On a clinical level, the lateralization of components of human olfaction is contrasted by the predominantly birhin...
Deep(er) Learning.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jul 13, 2018
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...
Animals
Deep Learning
Models, Neurological
Reward
Odorants
Synapses
Olfactory Receptor Neurons
Signal Detection, Psychological
Environment
Neuropil
Conditioning, Operant
Signal-To-Noise Ratio
Reinforcement, Psychology
Afferent Pathways
Drosophila melanogaster
Conditioning, Classical
Mushroom Bodies
Olfactory Perception
Avoidance Learning
Nerve Net
Association Learning
Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.
Food research international (Ottawa, Ont.)
Oct 13, 2017
In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by do...
Machine-learned pattern identification in olfactory subtest results.
Scientific reports
Oct 20, 2016
The human sense of smell is often analyzed as being composed of three main components comprising olfactory threshold, odor discrimination and the ability to identify odors. A relevant distinction of the three components and their differential changes...
Demixed principal component analysis of neural population data.
eLife
Apr 12, 2016
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information...
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