Circuit mechanisms of learning and action selectionGroup Leader Page
A key function of the nervous system is selecting what to do next based on combinations of rewarding and aversive features in the environment. To be able to 1) learn which stimuli predict rewards and punishments; 2) compute the “value” each action could provide, based on all the stimuli present; and 3) select one action and suppress other physically incompatible competing actions. The goal of the available projects is to elucidate the circuit principles underlying these three key functions.
One obstacle to progress in the field has been the problem of identifying underlying circuit motifs, and causally relating these structural motifs to their proposed function. Furthermore, decisions often emerge from parallel and distributed computation across many interconnected brain areas, requiring comprehensive analysis of structure and function across multiple areas.
We can overcome these obstacles by working in the tractable model system, the Drosophila larva. Drosophila larvae have a complex nervous system with more than 12,000 neurons and they can form robust memories and select actions based on memories. Because the nervous system is very compact it can be sectioned and imaged with modern electron microscopes and large distributed circuits can be mapped with synaptic resolution. Neural activity in all neurons at once during learning and decision-making can be monitored by calcium imaging using light-sheet microscopy. Finally, an exquisite genetic toolkit allows selective manipulation of individual neuron types so that causal relationships between neural activity and behavior can be established. The projects will combine these techniques to investigate the circuit basis of learning and action selection.
Eichler K.f , Li F.f , Kumar A. L., Andrade I., Schneider-Mizell C., Saumweber T., Huser A., Gerber, B., Fetter R. D., Truman J. W., Abbott L. F., Thum A., Zlatic, M.c and Cardona A.c. (2017)
The complete connectome of a learning and memory centre in an insect brain.
Nature 548(7666):175-182. doi: 10.1038/nature23455.
Eschbach C., Fushiki A., Winding M., Schneider-Mizell C. M., Shao M., Arruda B., Eichler K., Valdes-Aleman J., Thum A. S., Gerber B., Fetter R. D., TrumanW. J., Litwin-Kumar A., Cardona A. and Zlaticm M (2019)
Multilevel recurrent architecture for adaptive regulation of learning in the insect brain.
Ohyama T.f , Schneider-Mizell, C.f , Fetter, R. D., Valdez-Aleman J., Francoville R., Rivera Alba M., Mensh, B., Simpson, J. H., Branson, K., Truman, J. W., Cardona, A.c and Zlatic M.c (2015)
Multilevel multimodal integration enhances action selection.
Nature 520: 633-639.
Jovanic T.f , Schneider-Mizell, C.f , Shao M., Masson J.-B., Denisov G., Fetter R. D., Truman J. W., Cardona, A.c and Zlatic M.c. (2016)
Competitive disinhibition in early sensory processing mediates behavioral choice and sequences in Drosophila.
Cell 167: 1-13.
Vogelstein J. T.f , Park Y.f , Ohyama T.f , Kerr R. A., Truman J.W., Priebe C. E.c and Zlatic M.c (2014)
Discovery of brain-wide neural-behavior maps via multiscale unsupervised structure learning.
Science 344(6182): 386-92.