Model of the HVC neural network as a song motor in zebra finch

Abstract

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The nucleus HVC within the avian song system produces crystalized instructions which lead to precise, learned vocalization in zebra finches (Taeniopygia guttata). This paper proposes a model of the HVC neural network based on the physiological properties of individual HVC neurons, their synaptic interactions calibrated by experimental measurements, as well as the synaptic signal into this region which triggers song production. This neural network model comprises of two major neural populations in this area: neurons projecting to the nucleus RA and interneurons. Each single neuron model of HVCRA is constructed with conductance-based ion currents of fast Na+ and K+ and a leak channel, while the interneuron model includes extra transient Ca2+ current and hyperpolarization-activated inward current. The synaptic dynamics is formed with simulated delivered neurotransmitter pulses from presynaptic cells and neurotransmitter receptor opening rates of postsynaptic neurons. We show that this network model qualitatively exhibits observed electrophysiological behaviors of neurons independent or in the network, as well as the importance of bidirectional interactions between the HVCRA neuron and the HVCI neuron. We also simulate the pulse input from A11 neuron group to HVC. This signal successfully suppresses the interneuron, which leads to sequential firing of projection neurons that matches measured burst onset, duration, and spike quantities during the zebra finch motif. The result provides a biophysically based model characterizing the dynamics and functions of the HVC neural network as a song motor, and offers a reference for synaptic coupling strength in the avian brain.

Keywords

• computational model
• HVC
• neuron interaction
• song system
• zebra finch