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), the primary cortical target of OB projections, remains unknown. Here, we developed a biophysically relevant PCx network model and simulated it using previously recorded in vivo activities of mitral and tufted cells from OB in response to 2Hz and 20Hz odor frequencies for three odor mixtures. Analysis of the pyramidal cells’ (PYRs) activity during the initial 500ms of odor stimulation revealed that 4.4-11.8% of cells exhibited significantly different average firing rates for the two different frequencies. Furthermore, 1D convolutional neural network (CNN) models trained and tested on PYRs’ activities achieved discrimination accuracies of 73-95%. However, cells showing significant frequency discrimination contributed similarly to CNN performance as the non-discriminating cells. Using virtual synaptic knockout models, we found that eliminating either feedback or feedforward inhibition onto PYRs improved CNN decoding accuracy across all odor conditions. Conversely, eliminating recurrent excitation among pyramidal neurons or eliminating recurrent inhibition among both interneuron types simultaneously degraded CNN accuracy. Removing recurrent connections within individual interneuron populations had minimal effects on the performance. 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.