Biophysically relevant network model of the piriform cortex predicts odor frequency encoding using network mechanisms

Olfactory-guided animals utilize fast concentration fluctuations in turbulent odor plumes to perceive olfactory landscapes. Recent studies demonstrate that the olfactory bulb (OB) encodes such temporal features present in natural odor stimuli. However, whether this temporal information is encoded in the piriform cortex (PCx) remains unknown. Hence, we developed a biophysically relevant PCx network model and simulated it using previously recorded in vivo activities of mitral and tufted cells in response to 2Hz and 20Hz odor frequencies for three odor mixtures. Analysing the single-cell activity across trials revealed that individual pyramidal neurons (PYRs) were ineffective at distinguishing between 2Hz vs. 20Hz. However, the trial-averaged activity of the PYRs population could discriminate between the two frequencies significantly. Moreover, using log-likelihood scores we further discovered that odor frequency discrimination happened through a highly distributed mechanism among the PYRs. 1-D convolutional neural network (CNN) models trained and tested on PYRs activities achieved discrimination accuracies up to 95%. Using virtual synaptic knockout models, we found that eliminating either feedback or feedforward inhibition onto PYRs improved the decoding accuracy across all odor conditions. Conversely, eliminating recurrent excitation among PYRs or simultaneously eliminating recurrent inhibition among both interneuron types degraded the accuracy. Removing recurrent connections within individual interneuron populations had minimal effects on the performance. Overall, our PCx model demonstrates that it can discriminate between 2Hz and 20Hz odor stimuli, with a bidirectional capability of performance modulation by specific circuit motifs. These findings predict that the piriform cortex encodes and processes temporal features of odor stimuli.