skip to primary navigationskip to content

Neural circuit mechanisms of memory-based decision-making

Neural circuit mechanisms of memory-based decision-making 

Supervisor: Dr Marta Zlatic


Project summary:

Animals are born with innate representations of valences of many sensory cues, acquired through evolution. Some odors are
innately attractive and others are innately repulsive.  However, to behave adaptively in an ever-changing environment, animals are also able to learn new valances for sensory cues. Furthermore, the new, learnt valances can be in conflict with the innate ones. For example, repeated association of an innately attractive food odor with negative consequences (eg. pain, or illness) allows a switch from innate attraction to learnt aversion of the same odor. The representations of innate and learnt valences are thought to be stored in separate brain areas in both vertebrates and invertebrates. However, the way in which these representations are integrated to produce a coherent behavioral choice is unknown. How can the learnt valences override conflicting innate valences? What are the circuit mechanisms that enable a switch from innate attraction to learnt avoidance?  The aim of this project is to address these open questions using the tractable genetic model system, the Drosophila larva. Insects, especially their larval stages, have smaller and more compact brains, readily amenable to large-scale electron microscopy circuit mapping, but are also capable of associative learning. In Drosophila larva, we can therefore combine large scale electron microscopy reconstruction with targeted manipulation of uniquely identified neuron types and calcium imaging and electrophysiological recordings in identified neurons to uncover the circuit mechanisms of behavioural choice1,2,3,4,5.


What the student will be doing:

The project will involve a number of cutting edge techniques: high-throughput automated behavioural experiments (training
animals to associate rewards or punishments with specific odors), calcium imaging of neural activity, patch-clamp recordings and optogenetic manipulation of identified neurons



1. Vogelstein J. T., Park Y.f , Ohyama T., Kerr R. A., Truman J.W., Priebe C. E. and Zlatic M. (2014): Discovery of brain-wide neural-behavior maps via multiscale unsupervised structure learning. Science 344(6182): 386-92.

2. Ohyama T., Schneider-Mizell, C., Fetter, R. D., Valdez-Aleman J., Francoville R., Rivera Alba M., Mensh, B., Simpson, J. H., Branson, K., Truman, J. W., Cardona, A. and Zlatic M. (2015): Multilevel multimodal integration enhances action selection. Nature 520: 633-639.

3. 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.

4 Eichler K., Li 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. and Cardona A.. The complete connectome of a learning and memory center in an insect brain (2017). Nature 548, 175–182. doi:10.1038/nature23455

5. How to map the circuits that define us (2017). Nature 548, 150–152. doi:10.1038/548150a



Wellcome Trust Investigator Award