Parvalbumin Neurons and Cortical Coding of Dynamic Stimuli: A Network Model

Abstract Cortical circuits feature both excitatory and inhibitory cells that underlie the encoding of dynamic sensory stimuli, e.g., speech, music, odors, and natural scenes. While previous studies have shown that inhibition plays an important role in shaping the neural code, how excitatory and inhibitory cells coordinate to enhance encoding of temporally dynamic stimuli is not fully understood. Recent experimental recordings in mouse auditory cortex have shown that optogenetic suppression of parvalbumin neurons results in a decrease of neural discriminability of dynamic stimuli. Here, we present a multilayer model of a cortical circuit that mechanistically explains these results. The model is based on parvalbumin neurons which respond to both stimulus onsets and offsets, as observed experimentally, and incorporates characteristic shortterm synaptic plasticity profiles of excitatory and parvalbumin neurons. We reveal that by tuning the relative strengths of onset and offset inputs to parvalbumin neurons, the model generates different regimes of coding dominated by rapid firing rate modulations or spike timing. Moreover, the model replicates the experimentally observed reduction in neural discrimination performance during optogenetic suppression of parvalbumin neurons. These results suggest that distinct onset and offset inputs to parvalbumin neurons enhance cortical discriminability of dynamic stimuli by encoding distinct temporal features, enhancing temporal coding, and reducing cortical noise. New & Noteworthy Here we propose a model for the mechanisms that underlie neuron responses in the auditory cortex. This study focuses on single channel tuning of simple interactions between neurons. Using physiologically relevant parameters that underly parvalbumin and excitatory neurons, in the proposed artificial network, we show that we can recreate observed results in live studies.