P087 Structured Inhibition and Excitation in HVC: A Biophysical Approach to Song Motor Sequences
Fatima H. Fneich*1, Joseph Bakarji2,Arij Daou1
1Biomedical Engineering Program, American University of Beirut, Lebanon 2Department of Mechanical Engineering, American University of Beirut, Lebanon
*Email: fhf07@mail.aub.edu
Introduction Stereotyped neural sequencesoccurin thebrain [1], yet the neurophysiological mechanisms underlying their generation remain unclear. Birdsong is a prominent model to study such behavior, as juvenile songbirds learn from tutors andlaterproduce stereotyped song patterns. The premotor nucleus HVC coordinates motor and auditory activity for learned vocalizations.HVC consists of three neural populations with distinct in vitro and in vivo electrophysiologicalresponses [2,3]. Existing models explain HVC’s networkusingintrinsic circuitry, extrinsic feedback, or both. Here, we develop a physiologically realistic neural network model incorporating the three classes of HVC neurons basedonpharmacologicallyidentifiedion channels and synaptic currents. Methods We developed a conductance-based Hodgkin-Huxley-type model of HVC neurons and connected them via biologically realistic synaptic currents. The network was structured as a feedforward chain of microcircuits encoding sub-syllabic song segments, interacting through structured feedback inhibition[4]. Simulations were performed using MATLAB’s ode45 solver, incorporating key ionic currents, including T-type Ca²⁺, Ca²⁺-dependent K⁺, A-type K⁺, and hyperpolarization-activated inward current. Parameters were adjusted to replicate in vivo-like activity.The model reproduces sequential propagation of neural activity, highlighting intrinsic neuronal properties and synaptic interactions essential for song production. Results The model reproduced in vivo activity patterns of HVC neuron classes. HVCRA neuronsexhibitedsparse, time-locked bursts, each lasting ~10ms.HVCX neurons generated 1-4 bursts, typically following inhibitory rebound, while HVCINT neurons displayed tonic activity interspersed with bursts.Sequential propagation wasmaintainedthrough structured inhibition and excitation, with synapticconductancetuned to match dual intracellular recordings.The model accurately captured burst timing, spike shapes, and firing dynamicsobservedin experimental recordings, confirming its ability to simulate biologically realistic song-related neural activity. Discussion Our model provides a biophysically realistic representation of sequence generation in HVC, emphasizing the role of intrinsic properties and synaptic connectivity. The structured inhibition from HVCINT neurons ensured precise burst timing in HVCRA and HVCX neurons, supporting stable propagation. Key ionic currents, including T-type Ca²⁺ and A-type K⁺, regulated burst initiation and duration. These findings refine existing models by incorporating experimentallyobservedbiophysical details. This work offers new insights into the neural basis of motor sequence learning and could inform studies of other stereotyped behaviors.
Acknowledgements This work was supported by the University Research Board (URB) and the Medical Practice Plan (MPP) grants at the American University of Beirut.
