Mixture coding and segmentation in the anterior piriform cortex

Abstract

Airborne chemicals emitted by multiple objects, mix in the air prior to reaching the nose, complicating the recognition of specific odors of interest. The olfactory system is therefore faced with the task of identifying objects in the presence of rich and often unpredictable backgrounds. Piriform cortex is thought to be the site of object recognition and scene segmentation, yet the nature of its responses to odorant mixtures is largely unknown. In this study we asked two related questions. 1) How do mixture representations relate to the representations of mixture components? And 2) Can the identity of mixture components be readout from mixture representations in piriform cortex? To answer these question, we recorded single unit activity in the piriform cortex of naïve mice while sequentially presenting single odorants and their mixtures. We find that the magnitude of piriform cortical responses increases with added mixture components, and that the responses of individual neurons are well explained by a normalization model. Finally, we show that mixture components can be identified from piriform cortical activity by pooling responses of a small population of neurons. These results suggest that piriform cortical representations are well suited to perform figure-background segmentation without the need for learning.

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

“…The overlapping nature of sensory representations in the olfactory epithelium provides opportunity for many non-linear odorant interactions (Araneda et al, 2000; Malnic et al, 1999; Meister and Bonhoeffer, 2001; Rubin and Katz, 1999; Soucy et al, 2009). Interaction between odorants have been studied mostly at the level of sensory neurons (Duchamp-Viret et al, 2003; Firestein and Shepherd, 1992; Kurahashi et al, 1994; Oka et al, 2004; Pfister et al, 2020; Rospars et al, 2008; Singh et al, 2019; Takeuchi et al, 2009; Xu et al, 2020; Zak et al, 2020) but also, a bit more anecdotally, in the olfactory bulb and cortex (Davison and Katz, 2007; Fletcher, 2011; Giraudet et al, 2002; Gupta et al, 2015; Kadohisa and Wilson, 2006; Lei et al, 2006; Penker et al, 2020; Stettler and Axel, 2009; Wilson, 2003; Yoshida and Mori, 2007). These studies analyzed the amplitudes of responses to mixtures of odorants and found that these are typically sublinear.…”

State-dependent representations of mixtures by the olfactory bulb

“…Previous studies observed that the saturation effect of mixture summation can be modelled by a normalization model (Mathis et al, 2016;Penker et al, 2020). The equation of normalization (Penker et al, 2020) was:…”

“…In addition to the interactions in the periphery, there are many forms of nonlinear summation at multiple stages of olfactory processing, including the saturation of neural responses (Firestein et al, 1993;Wachowiak and Cohen, 2001) and inhibitory interactions in the olfactory bulb (Economo et al, 2016). Widespread suppressive interactions are also observed in downstream areas, including the piriform cortex (Penker et al, 2020;Stettler and Axel, 2009). Nonlinear summation of signals in some brain areas is desirable, for example, when specific combinations of signals carry special meanings (Agmon-Snir et al, 1998;Jacob et al, 2008).…”

“…Supplementary figure 2: No difference in residual nonlinear summation after normalisation (A) Neural responses are often normalized due to saturation of cellular mechanisms as well as actions by local inhibitory circuits. (B) Amplitude-dependent, saturation effect was removed by fitting the data using a standard normalisation model (Penker et al, 2020)…”

Target‐specific control of piriform cortical output via distinct inhibitory circuits

“…The recurrent pathway in APC is important for a variety of functions including odor concentration-invariant coding 2,3 and mixture demixing. 4 Recurrent excitatory inputs exhibit robust long-term synaptic plasticity, [5][6][7] which could be important for context-dependent retrieval and storage of memory. 6,[8][9][10][11][12] Hence, neurons in APC can extract relevant information about odors depending on context, but how local circuits achieve this is largely unknown.…”