Balancing the Robustness and Efficiency of Odor Representations during Learning

• Published in: Neuron (Cambridge, Mass.) Mass.), 2016-10, Vol.92 (1), p.174-186
• Main Authors: Chu, Monica W., Li, Wankun L., Komiyama, Takaki
• Format: Article
• Language: English
• Subjects: Animals; Cadherins - genetics; Discrimination (Psychology) - physiology; Efficiency; Learning - physiology; Male; Mice; Mice, Transgenic; Neurons; Neurons - physiology; Odorants; Odors; Olfactory Bulb - physiology; Olfactory Perception - physiology; Population; Principal components analysis; Smell
• ISSN: 0896-6273; 1097-4199
• DOI: 10.1016/j.neuron.2016.09.004

For reliable stimulus identification, sensory codes have to be robust by including redundancy to combat noise, but redundancy sacrifices coding efficiency. To address how experience affects the balance between the robustness and efficiency of sensory codes, we probed odor representations in the mouse olfactory bulb during learning over a week, using longitudinal two-photon calcium imaging. When mice learned to discriminate between two dissimilar odorants, responses of mitral cell ensembles to the two odorants gradually became less discrete, increasing the efficiency. In contrast, when mice learned to discriminate between two very similar odorants, the initially overlapping representations of the two odorants became progressively decorrelated, enhancing the robustness. Qualitatively similar changes were observed when the same odorants were experienced passively, a condition that would induce implicit perceptual learning. These results suggest that experience adjusts odor representations to balance the robustness and efficiency depending on the similarity of the experienced odorants. •Learning modifies the robustness and efficiency of odor codes in olfactory bulb•Learning of difficult discrimination enhances robustness•Learning of easy discrimination enhances efficiency•Task learning and passive exposure induce qualitatively similar changes Robustness and efficiency are antagonistic factors that affect the effectiveness of sensory codes. Using longitudinal two-photon calcium imaging, Chu et al. (2016) find that learning balances the robustness and efficiency of olfactory bulb odor codes bidirectionally depending on odorant similarity.