Computational Neural Modeling of Auditory Cortical Receptive Fields

• Published in: Frontiers in computational neuroscience Vol. 13; p. 28
• Main Authors: Chambers, Jordan D, Elgueda, Diego, Fritz, Jonathan B, Shamma, Shihab A, Burkitt, Anthony N, Grayden, David B
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 24.05.2019; Frontiers Media S.A
• Subjects: Acoustics; auditory cortex; Auditory discrimination; Auditory plasticity; Behavioral plasticity; Cochlea; Computational neuroscience; Computer applications; Cortex (auditory); genetic algorithm; Investigations; mathematical modeling; Neural networks; Neural plasticity; Neurons; Neuroscience; Perception; Plasticity (neural); Receptive field; Sound; spectrotemporal receptive fields (STRFs); genetic algorithm; mathematical modeling; spectrotemporal receptive fields (STRFs); auditory cortex; neural networks
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2019.00028

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 how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. 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. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive cortical neurons and/or synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in 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 behaviorally salient sounds.