Inhibition, recurrent excitation, and neural feedback in computational models of sparse bursting and birdsong sequencing

Mapping the functional diversity of interneurons and uncovering the roles of neural feedback in the brain are two active areas of experimental research. The telencephalic nucleus HVC is situated at a critical point in the pattern-generating premotor circuitry of oscine songbirds and receives neural feedback from both forebrain and brainstem areas. A striking feature of HVC's premotor activity is that its projection neurons burst extremely sparsely. In Chapter 1 of this dissertation, I present a computational model of HVC embodying several central hypotheses: (1) sparse bursting is generated in bistable groups of recurrently connected RA-projecting (HVCRA< /sub>) neurons; (2) inhibitory interneurons terminate bursts in the HVCRA groups; and (3) sparse sequences of bursts are generated by the propagation of waves of bursting activity along networks of HVCRA neurons. This model of sparse bursting places HVC in the context of central pattern generators and cortical networks utilizing inhibition, recurrent excitation, and bistability. Importantly, the unintuitive result that inhibitory interneurons can precisely terminate the bursts of HVCRA groups while showing relatively sustained activity throughout the song is made possible by a specific constraint on their connectivity. I use the model to make novel predictions that can be tested experimentally. In Chapter 2, I present a computational model of HVC and associated nuclei that builds on the model of sparse bursting presented in Chapter 1. This model embodies the hypotheses that (1) different networks in HVC control different syllables or notes of birdsong, (2) interneurons in HVC not only participate in sparse bursting but also provide mutual inhibition between networks controlling syllables or notes, and (3) these syllable networks are stimulated by neural feedback via the brainstem and the afferent thalamic nucleus Uva, or a similar feedback pathway. I discuss the model's ability to unify physiological, behavioral, and lesion results, and I use it to make novel predictions that can be tested experimentall