The proposed research will initiate a cross-institutional collaboration between an electrophysiologist and a computational neuroscientist to examine fundamental questions about how the brain produces complex sequences of behaviors. Research will be conducted using songbirds, animals that produce a richly structured sequence of highly reproducible song syllables. Previous studies have recorded the activity individual brain cells while birds are singing, and have shown that neural activity is locked to song production at a temporal scale of several thousands of a second, a level of precision rarely achieved in the study of complex natural behaviors. This proposal will exploit this precise relationship between brain activity and sequential behavior by recording from individual birds as they sing many song renditions. Sophisticated analysis tools will then be used to examine both brain activity and song output in great detail. Subtle variations in syllable features and syllable sequencing will be used as ?natural experiments? to determine how the precise activity of individual nerve cells are grouped together to form behavioral units (the syllables), and how these groupings of neural activity are strung together into syllable sequences. Guided by these data, computer models will be constructed to better understand the underlying biological mechanisms that orchestrate complex sequences of brain activity. As part of the proposed interdisciplinary research, novel algorithms for the fine-grained analysis of vocal behavior will be developed, and these may find application in related fields ranging from motor control to robotics to human speech. The proposal also supports a cross-institutional student exchange between the University of Texas at San Antonio, a Hispanic-Serving Institution, and the University of California San Francisco, a world-renowned center for biomedical research.
Sequencing of discrete actions characterizes most animal behaviors, yet we still have a poor understanding of the extent to which discrete actions and their organization into sequences are controlled by the brain. Such an understanding is not only relevant to understanding broad aspects of movement control in many systems but is likely also relevant to understanding non-motor aspects of cognition. Like movements, thought processes require the sequential patterning of activity in which individual semantic elements are flexibly organized to achieve cognitive goals. Our findings regarding sequencing in the song system, where these elements and their sequencing are highly quantifiable, thus provides fodder for understanding similar processes across species and contexts. One prominent hypotheses in the field of song learning is that sequencing is determined by neural populations that are active at the end of one syllable triggering the start of the next syllable. At sequence branch points it is commonly hypothesized that transitions are made to the first syllable representation that reaches a threshold for activation. Furthermore, this hypothesis is local in time in that sequence transitions are driven exclusively by activity occurring within tens of milliseconds of the transition. Our findings challenge this view and provide new insights into the control of sequential behaviors. We find that timing and transition probability are related, but not in a manner consistent with a race model. This suggests that the presence of another causal link between sequencing and timing at branch points. Moreover, we find that the sequences in which gaps occur influence the temporal properties of those gaps. In some cases, this includes long term 'history dependence' in which the sequences that are produced much earlier in song may correlate with temporal properties of the gaps that occur at points of variable transitions in song. Taken together, our results indicate that complete models of sequencing (as well as investigations of underlying neural mechanisms) must account both for long time interactions between sequence history and the temporal and acoustic events at points of variability in sequences (branch points) as well as for non-temporally homogeneous relationships between sequencing of elements and the timing of elements.
