Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration
- New York University, United States
Abstract
To better understand how organisms make decisions on the basis of temporally varying multi-sensory input, we identified computations made by Drosophila larvae responding to visual and optogenetically induced fictive olfactory stimuli. We modeled the larva's navigational decision to initiate turns as the output of a Linear-Nonlinear-Poisson cascade. We used reverse-correlation to fit parameters to this model; the parameterized model predicted larvae's responses to novel stimulus patterns. For multi-modal inputs, we found that larvae linearly combine olfactory and visual signals upstream of the decision to turn. We verified this prediction by measuring larvae's responses to coordinated changes in odor and light. We studied other navigational decisions and found that larvae integrated odor and light according to the same rule in all cases. These results suggest that photo-taxis and odor-taxis are mediated by a shared computational pathway.
Article and author information
Author details
Reviewing Editor
- Ronald L Calabrese, Emory University, United States
Version history
- Received: December 22, 2014
- Accepted: May 5, 2015
- Accepted Manuscript published: May 6, 2015 (version 1)
- Version of Record published: June 16, 2015 (version 2)
Copyright
© 2015, Gepner et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration
eLife 4:e06229.
https://doi.org/10.7554/eLife.06229
Further reading
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- Computational and Systems Biology
- Neuroscience
Three recent studies use optogenetics, virtual ‘odor-scapes’ and mathematical modeling to study how the nervous system of fruit fly larvae processes sensory information to control navigation.
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- Neuroscience
Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.
