Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC

Abstract

Stereotyped neural sequences are often exhibited in the brain, yet the neurophysiological mechanisms underlying their generation are not fully understood. Birdsong is a prominent model to study such behavior particularly because juvenile songbirds progressively learn from their tutors and by adulthood are able to sing stereotyped song patterns. The songbird premotor nucleus HVC coordinate motor and auditory activity responsible for learned vocalizations. The HVC comprises three neural populations that has distinct in vitro and in vivo electrophysiological responses. Typically, models that explain HVCs network either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. Here, we developed a physiologically realistic neural network model incorporating the three classes of HVC neurons based on the ion channels and the synaptic currents that had been pharmacologically identified. Our model is based on a feedforward chain of microcircuits that encode for the different sub-syllabic segments (SSSs) and that interact with each other through structured feedback inhibition. The network reproduced the in vivo activity patterns of each class of HVC neurons, and unveiled key intrinsic and synaptic mechanisms that govern the sequential propagation of neural activity by highlighting important roles for the T-type Ca2+ current, Ca2+-dependent K+ current, A-type K+ current, hyperpolarization activated inward current, as well as excitatory and inhibitory synaptic currents. The result is a biophysically realistic model that suggests an improved characterization of the HVC network responsible for song production in the songbird. Significance StatementLearned motor sequences acquired through repetitive practice undergo stabilization and are integrated into cortical motor circuits through a process of consolidation. The process of mastering a complicated motor sequence requires thorough motor exploration to achieve closer alignment with the desired outcome, yet the neural mechanisms underlying sequence generation remain largely unexplored. In this study, we investigate the neural circuitry in a premotor region of the songbird brain (known as HVC) that is well-known for generating extremely precise learned sequences. We develop a biophysically realistic network model that incorporate pharmacologically identified intrinsic and synaptic currents for the three different classes of HVC neurons. The network highlights fundamental intrinsic and synaptic mechanisms regulating the sequential propagation of neural activity.