P067 Optimization-Based Insights into Network Configurations of the Songbird Premotor Cortex
Fatima M. Dia*1, Maher Nouiehed2, Arij Daou1
1Department of Biomedical Engineering, American University of Beirut, Beirut, Lebanon 2Department of Industrial Engineering and Management, American University of Beirut, Beirut, Lebanon
*Email:fmd14@mail.aub.edu
Introduction Neural circuits in the brain maintain a delicate equilibrium between excitation and inhibition, yet how this balance operates remains unclear[1]. Moreover, neural circuits often exhibit sequences of activity that rely on excitation and inhibition, but the contribution of local networks to their generation is not well understood. This study investigates neural sequence generation within the High Vocal Center (HVC) of the adult zebra finch forebrain. The HVC plays a critical role in the execution of temporally precise courtship songs and is comprised of three neural populations with distinct electrophysiological responses: glutamatergic basal ganglia–projecting (HVCX) and forebrain-projecting (HVCRA) cortical neurons, and GABAergic interneurons (HVCINT)[2]. While the connections between these neuronal classes are known[1,3], how they orchestrate this temporally precise neural sequence remains largely unknown. Methods To address this question, we applied optimization techniques and mathematical modeling to describe the relationships among HVCRA, HVCX, and HVCINT neurons and their bursting patterns. Our approach focused on uncovering the underlying cytoarchitecture of the HVC neural network by utilizing biologically realistic constraints. These constraints included the pharmacological nature of synaptic connections, anatomical and intrinsic properties, neuronal population ratios, precise burst timing, and spiking frequency during song motifs[2,4]. The study incorporated both closed and open network configurations to assess their ability to reproduce observed bursting sequences. Results Our computational framework successfully predicted the minimalistic synaptic connections required to replicate the observed bursting patterns of the HVC network. The model identified specific network topologies that satisfied experimental constraints while maintaining functional output. Additionally, our findings indicated that certain network configurations necessitate additional nodes to form a fully connected network capable of sustaining stable sequential bursting. These predictions align with previous experimental data and provide novel insights into potential connectivity motifs that could underlie the temporal precision of song production. Discussion This study bridges experimental data with computational predictions, offering a framework for understanding how local excitatory and inhibitory interactions within HVC generate precise neural sequences. By identifying minimal network configurations, our model provides a hypothesis regarding the synaptic architecture required for sequence generation. Future work should incorporate in vivo validation of the predicted connectivity patterns using electrophysiological and optogenetic approaches. Our findings contribute to a broader understanding of how premotor circuits coordinate motor behaviors and may have implications for studying sequence generation in other brain regions beyond the songbird HVC.
Acknowledgements This work was supported by the University Research Board (URB) and the Medical Practice Plan (MPP) grants at the American University of Beirut. References ● https://doi.org/10.1152/jn.00162.2013 ● https://doi.org/10.1038/nature00974 ● https://doi.org/10.1038/nature09514 ● https://doi.org/10.1152/jn.00952.2006
