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Connectomic analysis of local HVC circuitry in the zebra finch


Narayanan,  RT
Max Planck Institute for Biological Cybernetics, Max Planck Society;


Oberlaender,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Benezra, S., Kornfeld, J., Narayanan, R., Oberlaender, M., Denk, W., & Long, M. (2015). Connectomic analysis of local HVC circuitry in the zebra finch. Poster presented at 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), Chicago, IL, USA.

The zebra finch song is a complex motor sequence with important parallels to human skilled behavior, such as speech and musical performance. Lesions to the cortical nucleus HVC abolish singing behavior, suggesting that this region plays a critical role in the production of song (Nottebohm et al., 1976). Single premotor neurons in HVC exhibit a high frequency burst of action potentials at one time point during singing, and different cells are often active at different moments in the song (Hahnloser et al., 2002; Long et al., 2010; Vallentin and Long, 2015). Cooling HVC slows song speed across all timescales, suggesting that connectivity within HVC is important for generating these premotor sequences (Long and Fee, 2008). Despite previous electrophysiological studies (Mooney and Prather, 2005; Kosche et al. 2015), we know very little about the local wiring rules within that nucleus, in part because of an almost total lack of knowledge concerning the fine structure of single HVC premotor neurons (Gurney and Katz, 1981; Mooney, 2000). To address this, we conducted an anatomical study of these cells using a strategy that combines light (LM) and electron microscopy (EM) approaches. We filled 15 premotor neurons in vivo with single-cell Neurobiotin injections and reconstructed the entirety of these processes throughout the nucleus. We also used serial block-face EM to examine the synaptic properties of labeled premotor neurons within a 150x150x60 µm volume. Together, these reconstructions enabled us to quantify the distribution and identity of presynaptic inputs along the dendrites and to estimate the total number of synapses received by single premotor neurons. To examine the number of postsynaptic targets for each HVC premotor neuron, we quantified bouton distribution along axon collaterals in LM. EM reconstructions were used to assess the extent to which boutons reflect the presence of a synapse. Furthermore, we were able to distinguish the cell identity of many confirmed postsynaptic targets. Because a prominent model of HVC sequence generation relies upon strong feedforward monosynaptic connections between premotor neurons to form a ‘synaptic chain’ (Long et al., 2010), we were especially interested in examining synapses of this nature. Our approach enabled us to identify a large number of premotor-premotor contacts and to characterize their distribution along various dendritic branches. Taken together, these data will inform computational models of information flow within HVC and help to establish a mechanistic understanding of the circuitry underlying other forebrain sequence generating circuits.