Hyperbolic geometry of the olfactory space

This week on Journal Club session Emil Dmitruk talk about the paper "Hyperbolic geometry of the olfactory space".

In the natural environment, the sense of smell, or olfaction, serves to detect toxins and judge nutritional content by taking advantage of the associations between compoundsas they are created in biochemical reactions. This suggests that the nervous system can classify odors based on statistics of their co-occurrence within natural mixtures rather than from the chemical structures of the ligands themselves. We show that this statistical perspective makes it possible to map odors to points in a hyperbolic space. Hyperbolic coordinates have a long but often underappreciated history of relevance to biology. For example, these coordinates approximate the distance between species computed along dendrograms and, more generally, between points within hierarchical tree–like networks. We find that both natural odors and human perceptual descriptions of smells can be described using a three-dimensional hyperbolic space. This match in geometries can avoid distortions that would otherwise arise when mapping odors to perception.

Papers:


Date: 07/02/2020
Time: 16:00
Location: B200

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neuroscience Computer Science COVID-19 PCA Persistent homology Convolutional Neural Networks Deep learning Associative memory Purkinje cell cerebellum olfaction computational neuroscience Neuromorphic hardware Dendritic computation Evolutionary algorithms Software development optimization Open position Structural plasticity neural networks robotics Neurons Studentship Neuronal Morphology Artificial intelligence computational modelling Homoeostasis Network connectivity synaptic plasticity machine learning