Balancing the Robustness and Efficiency of Odor Representations during Learning

Abstract

Summary 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.

Context- and Output Layer-Dependent Long-Term Ensemble Plasticity in a Sensory Circuit

“…The change in MC representation is consistent with a recent report (Chu et al., 2016), though we observed neither a strong sparsening of representation during learning (Figure 6) nor improved pattern separation during passive sensory experience (Figure 7). Furthermore, we showed that the ensemble pattern separation in MCs but not in TCs improves simultaneously as animals learn to discriminate perceptually similar odors (Figure 7).…”

“…Although the ensemble correlation analysis was not performed, this could translate as a weak and transient improvement of pattern separation in our setting, which would be in contrast to the gradual development of significant pattern separation that we observed in MCs. A potential source of this discrepancy could be the difficulty of the task: the sets of odorants these authors used for the task were easier to discriminate than ours and therefore might not necessarily involve improvement of pattern separation upon learning (Chu et al., 2016, Gödde et al., 2016, Gschwend et al., 2015). Moreover, with multi-unit recording, they could neither distinguish MCs and TCs nor track the same cell ensembles across multiple days, demonstrating the advantage of the chronic imaging technique we employed in the current study.…”

Leptin modulates olfactory discrimination and neural activity in the olfactory bulb

“…Previous studies have revealed that mice require extensive active learning, which likely triggers plasticity in the OB network, to disentangle similar sensory inputs during a difficult discrimination task. 33,42,43 Because the M/Ts, the granule cells, and their interactions are involved in the plasticity of the OB, [44][45][46] our findings suggest that leptin likely influences odor discrimination through its effects on these neurons and the OB network between them.…”

Circuit Mechanisms of Sensorimotor Learning

“…However, when mice discriminated between very dissimilar odorants, counterintuitively, the representations of the two odorants gradually became more similar. This bidirectional effect was interpreted such that learning achieves an optimal separation of representations of familiar stimuli, balancing the robustness of discrimination and capacity of coding (Chu et al, 2016). …”

“…The reduction in population responses may be a common feature of learning-driven changes in population coding (see also the motor skill learning section below), which could reduce overlaps between representations in space and time and facilitate decoding by downstream areas (Laurent, 2002). Indeed, chronic tracking of the same neural population over sensorimotor learning demonstrated a decrease in the number of responsive neurons and/or magnitudes of responses to the same sensory stimuli (Chu et al, 2016; Gdalyahu et al, 2012; Makino and Komiyama, 2015). …”