Parvalbumin neurons and cortical coding of dynamic stimuli: a network model.
Journal:
Journal of neurophysiology
Published Date:
May 13, 2025
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. Although 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 the 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 that respond to both stimulus onsets and offsets, as observed experimentally, and incorporates characteristic short-term synaptic plasticity profiles of excitatory and parvalbumin neurons. We also explore different network architectures consistent with experimental results. The model reveals that tuning the relative strengths of onset and offset inputs to parvalbumin neurons and network parameters 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. Here, we propose a model for the mechanisms that underlie neuron responses in the auditory cortex. This study focuses on a cortical circuit involving excitatory and inhibitory (parvalbumin) neurons. Using physiologically relevant parameters in the proposed model network, we show that we can recreate observed results in live studies.
