Abstract:
While the nonlinearity and complexity of biological phenomena keeps much of the intricate details of science mysterious, computational modeling can help unveil the dynamics and functionalities of such complex phenomena. The nucleus HVC (proper name) within the avian analog of mammal premotor cortex produces stereotyped instructions through the motor pathway leading to precise, learned vocalization by songbirds. The basal ganglia projecting HVC neurons (known as HVCX) are a major class of neurons that project to Area X (avian basal ganglia) and play an essential role in the orchestration of the neural circuitry that guides the bird’s learning and production mechanisms of his song. Huge efforts are being put onto developing realistic neural networks of HVC neurons and their associated networks, yet all of these modeling efforts are falling short due to the lack of appropriate models describing the intrinsic properties of HVCX neurons themselves. In this research, we developed a single compartment conductance-based model to replicate the neurophysiological firing patterns and properties seen the HVCX neurons of the zebra finch. The conductance-based model for HVC neurons was developed based on current-clamp data collected at the University of Chicago. Simulations of these model neurons were performed using MATLAB and XPPAUT. The modeled neurons are able to capture the spikes’ timing as well as the details of the spikes’ morphology and heuristics such as: spike amplitude, threshold, spike width, depolarization and repolarization segments. The careful fitting conducted unveiled important details about the intrinsic properties that these neurons exhibit in singing birds and built up on recent results that related ionic currents’ magnitudes to features of song. The result is an improved characterization of the HVCX neurons responsible for song production in the songbird and will be used in future studies to unveil the intricate circuitry that governs the overall process of bird’s song.
