Abstract
The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an ‘intelligent coincidence detector’, which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.
Highlights
Understanding how a brain encodes and decodes olfactory input has been an active field of study for decades [1,2]
In the mushroom body (MB), the target of the projection neurons (PNs), a small number of highly-specific Kenyon cells (KC) respond with shortlived activation periods, often only with a single spike. (ii) Odor-specific trajectories can be measured in the PN firing rate phase space, and the separation between the trajectories for different odors is greatest during a period of slow dynamics which lasts for about 1.5s after odor onset. (iii) The spatiotemporal patterns that arise in the PN population encode the identity of the odor [6], but can be difficult to differentiate for any two odors [7]
We developed a model of how the insect brain encodes the qualities of an odor and subsequently decodes this information to identify the odor
Summary
Understanding how a brain encodes and decodes olfactory input has been an active field of study for decades [1,2]. Some findings have been key in understanding how the insect brain makes sense of the olfactory information that it acquires from the outside world: (i) There are three stages of stimulus processing: in the antennae, the receptor neurons bond with odorants creating a time-invariant spatial pattern of activations in these neurons, which is sent to the antennal lobe [3]. (iii) The spatiotemporal patterns that arise in the PN population encode the identity of the odor [6], but can be difficult to differentiate for any two odors [7]. It is only at the KC level that the trajectories are identifiable, due to the sparseness of KC responses [2]
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