Computational Neural Modeling of Auditory Cortical Receptive Fields - Chambers, Jordan D.; Elgueda, Diego; Fritz, Jonathan B.; Shamma, Shihab A.; Burkitt, Anthony N.; Grayden, David B. (Frontiers in Computational Neuroscience, 2019)
Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate mechanisms by which cortical neurons can change their receptive fields. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation, which were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. This neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic drives in the model. The model predicts that altering synaptic drive between cortical neurons can account for rapid task-related changes observed experimentally by A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of important sounds in the presence of background noise.