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Disorder and the neural representation of complex odors: smelling in the real world

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abstract

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose a new interpretation of how the architecture of olfactory circuits is adapted to meet these immense complementary challenges. First, the diffuse binding of receptors to many molecules compresses a vast odor space into a tiny receptor space, while preserving similarity. Next, lateral interactions "densify" and decorrelate the response, enhancing robustness to noise. Finally, disordered projections from the periphery to the central brain reconfigure the densely packed information into a format suitable for flexible learning of associations and valences. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of olfactory information differs from the other senses in its fundamental use of disorder.

fields

cs.LG 1

years

2019 1

verdicts

UNVERDICTED 1

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  • Learning sparsity in reservoir computing through a novel bio-inspired algorithm cs.LG · 2019-07-19 · unverdicted · none · ref 20 · internal anchor

    A novel algorithm learns sparsity thresholds in reservoir computing via gradient descent on neuron-specific thresholds combined with MCMC on a global threshold, inspired by Drosophila neurobiology, and outperforms standard gradient descent on two tasks.