Abstract
Precise neural sequences are associated with the production of well-learned skilled behaviors. Yet, how neural sequences arise in the brain remains unclear. In songbirds, premotor projection neurons in the cortical song nucleus HVC are necessary for producing learned song and exhibit precise sequential activity during singing. Using cell-type specific calcium imaging we identify populations of HVC premotor neurons associated with the beginning and ending of singing-related neural sequences. We characterize neurons that bookend singing-related sequences and neuronal populations that transition from sparse preparatory activity prior to song to precise neural sequences during singing. Recordings from downstream premotor neurons or the respiratory system suggest that pre-song activity may be involved in motor preparation to sing. These findings reveal population mechanisms associated with moving from non-vocal to vocal behavioral states and suggest that precise neural sequences begin and end as part of orchestrated activity across functionally diverse populations of cortical premotor neurons.
Highlights
Using cell-type specific calcium imaging we identify populations of HVC premotor neurons associated with the beginning and ending of singing-related neural sequences
We used miniscope calcium imaging to examine the activity of populations of HVCRA neurons in singing zebra finches (Chen et al, 2013; Ghosh et al, 2011)
Previous studies suggested that the HVCRA network functions as a time-keeper, encoding motif-level temporal representations of song via propagation of precisely timed neural sequences (Hahnloser et al, 2002; Kozhevnikov and Fee, 2007; Long and Fee, 2008; Long et al, 2010; Lynch et al, 2016; Markowitz et al, 2015; Picardo et al, 2016)
Summary
The sequential activation of neurons is implicated in a wide variety of behaviors, ranging from episodic memory encoding and sensory processing to the voluntary production of skilled motor behaviors (Fee et al, 2004; Fiete et al, 2010; Hahnloser et al, 2002; Li et al, 2015; Lynch et al, 2016; Markowitz et al, 2015; Okubo et al, 2015; Peters et al, 2014; Rajan et al, 2016; Svoboda and Li, 2018).
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