2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-2B.21
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Calcium imaging in canary (serinus canaria) HVC reveals latent states supporting behavioral sequencing with long range history dependence
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1133-0
Authors: Yarden Cohen, Jun Shen, Dawit Semu, Timothy M. Otchy, Timothy J. Gardner, Boston University, United States
Abstract: History dependent behavior is a key readout of neural processing. In skills, like speech or dance, motor sequences follow syntactic rules in which transitions between motor elements rely on past actions. Canary songs are defined by syllable repeats, called phrases, whose syntax exhibits long range order. The phrase sequence neural underpinnings must either rely on fixed action patterns or maintain historic context to influence ongoing transitions. To discriminate such mechanisms, we recorded Ca2+ signals from the premotor nucleus HVC in freely behaving canaries. We find that song history is reflected in identified ROIs up to 4 phrases apart, spanning up to 3 seconds and 40 syllables and that some ROIs exhibit mixed history selectivity. Moreover, we find that signals, reflecting sequence history information are more frequent during phrase transitions that are history dependent compared to history insensitive ones. These findings suggest that the network dynamics reflects historic context relevant to flexible transitions. Additionally, we find ROIs whose signals last several seconds and span 3-4 phrases. These signals are rarely modulated by syllable or phrase boundaries and initiate mostly during stereotyped sequences, suggesting distinct network dynamics during stereotyped and variable behavior.