Temporal response properties of accessory olfactory bulb neurons: Limitations and opportunities for decoding

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Abstract

The vomeronasal system (VNS) is a major vertebrate chemosensory system that functions in parallel to the main olfactory system (MOS). Despite many similarities, the two systems dramatically differ in the temporal domain. While MOS responses are governed by breathing and follow a subsecond temporal scale, VNS responses are uncoupled from breathing and evolve over seconds. This suggests that the contribution of response dynamics to stimulus information will differ between these systems. While temporal dynamics in the MOS are widely investigated, similar analyses in the accessory olfactory bulb (AOB) are lacking. Here, we have addressed this issue using controlled stimulus delivery to the vomeronasal organ of male and female mice. We first analyzed the temporal properties of AOB projection neurons and demonstrated that neurons display prolonged, variable, and neuron-specific characteristics. We then analyzed various decoding schemes using AOB population responses. We showed that compared with the simplest scheme (i.e., integration of spike counts over the entire response period), the division of this period into smaller temporal bins actually yields poorer decoding accuracy. However, optimal classification accuracy can be achieved well before the end of the response period by integrating spike counts within temporally defined windows. Since VNS stimulus uptake is variable, we analyzed decoding using limited information about stimulus uptake time, and showed that with enough neurons, such time-invariant decoding is feasible. Finally, we conducted simulations that demonstrated that, unlike the main olfactory bulb, the temporal features of AOB neurons disfavor decoding with high temporal accuracy, and, rather, support decoding without precise knowledge of stimulus uptake time.

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Yoles-Frenkel, M., Kahan, A., & Ben-Shaul, Y. (2018). Temporal response properties of accessory olfactory bulb neurons: Limitations and opportunities for decoding. Journal of Neuroscience, 38(21), 4957–4976. https://doi.org/10.1523/JNEUROSCI.2091-17.2018

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