Evolving the Olfactory System with Machine Learning

This week on Journal Club session Nik Dennler will talk about a paper "Evolving the Olfactory System with Machine Learning".

The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity onto a larger expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.


Papers:

Date: 2021/10/29
Time: 14:00
Location: online

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