Social cognition; Neurophysiology; Songbird; Instrumentation; Behavior; Electrical stimulation; vocal learning; dreaming; social; family; zebra finch
Canopoli Alessandro, Zai Anja, Hahnloser Richard (2016), Lesions of a higher auditory brain area during a sensorimotor period do not impair birdsong learning, in Matters
Canopoli Alessandro, Herbst Joshua A, Hahnloser Richard H R (2014), A higher sensory brain region is involved in reversing reinforcement-induced vocal changes in a songbird., in The Journal of neuroscience : the official journal of the Society for Neuroscience
, 34(20), 7018-26.
Kollmorgen Sepp, Hahnloser Richard H R (2014), Dynamic alignment models for neural coding., in PLoS computational biology
, 10(3), 1003508-1003508.
Giret Nicolas, Kornfeld Joergen, Ganguli Surya, Hahnloser Richard H R (2014), Evidence for a causal inverse model in an avian cortico-basal ganglia circuit., in Proceedings of the National Academy of Sciences of the United States of America
, 111(16), 6063-8.
Anisimov Victor N, Herbst Joshua A, Abramchuk Andrei N, Latanov Alexander V, Hahnloser Richard H R, Vyssotski Alexei L (2014), Reconstruction of vocal interactions in a group of small songbirds., in Nature methods
, 11(11), 1135-7.
Hanuschkin A, Ganguli S, Hahnloser R H R (2013), A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models., in Frontiers in neural circuits
, 7, 106-106.
Hahnloser Richard, Ganguli Surya, Vocal learning with inverse models, in Rodrigo Quian Quiroga (ed.), CRC Press, USA.
Many complex learning behaviors such as speech learning are strongly influenced by factors including social context and sleep. Although many influences are known today, they have mostly been studied in artificial rather than natural settings and are currently supported only by correlative but not by causal evidence. Our work aims at bridging this gap by studying vocal development in the songbird and its dependence on social interactions with conspecific birds and on neural replay of behavioral sequences during sleep. The songbird is a vocal learner in which the brain mechanisms involved in sleep replay are well known. During sleep, premotor neurons of the vocal apparatus in zebra finches engage in bursting patterns that are reminiscent of their patterns generated during song production. In the past, we have performed extensive studies on the generation of such sleep-burst sequences in large song-control networks. We now want to make use of his knowledge and test for a causal relationship between sleep sequences and vocal development. Such a causal relationship has been widely hypothesized but has never been tested experimentally. We plan to implant stimu-lation electrodes into the brain area that generates sleep sequences and perturb downstream sleep bursts to study their effects on vocal changes. Comparisons will be made with birds that are similarly stimulated, but at daytime rather than during sleep. We hope this research will provide one of the first demonstrations that off-line neural activity during sleep has key roles for procedural learning.The social interactions during vocal learning and their influences have not been studied exten-sively yet. In a different set of experiments we will study the social factors that are beneficial or detrimental to vocal learning. It is known that birds with siblings produce less accurate copies of tutor song than birds without siblings, an effect known as a fraternal inhibition. However, the precise factors of this inhibition and the role of the parents in mediating this inhibition are currently not known. To study such effects of social context and many more, we will design and build a bird monitoring system in which several microphones and a video camera jointly operate to record all songs, the locations of the singers, and the locations of the other birds of a family living inside the same cage. This bird-monitoring system is a difficult instrumentation task because jointly tutored birds sing similar songs and often they sing at the same time. Hence, we will apply sophisticated machine-learning techniques to solve this blind-source separation problem. Our bird monitoring system will be fully automated and minimally rely on human input, thus facilitating behavioral experiments and high-throughput data acquisition. We are convinced that this tool will be useful to a large community of birdsong and neuroethology researchers worldwide, because one of the most successful strategies for understanding brain mechanisms is to study them in natural settings rather than in artificial ones. In future experiments beyond this grant application, we will make use of this bird-monitoring system also in electrophysiological experiments, to study brain mechanisms in a natural social context.The broader relevance of our work extends from songbirds to human social sciences and physi-ology, because effects of sibling number and parental interactions influence speech learning also in children. And, the functions of sleep and dreams remain deeply mysterious and any new insights even in animal models will be highly valuable.