Science and Mathematics

Cracking Neural Circuits of a Simple Brain

February 27, 2020 at 3:30pm5:00pm

Physics Building, 202 / 204

The Department of Physics welcomes Dr. Mirna Mihovilovic, a postdoctoral scientist at the Center for Soft Matter Research at NYU, for their weekly colloquia. This speaker is a candidate for a faculty position in the Department of Physics, part of the cluster hiring initiative in the BioInspired Institute.

Abstract: How do brains compute? The Drosophila (fruit fly) larva is a small, semi-transparent crawling organism with about 10,000 neurons, compared to 100 billion in humans and 100 million in mice. Despite this simplicity, the larva carries out information-processing tasks, including navigation – moving towards a favorable location based on information from its senses. A century of genetic work in Drosophila combined with recent innovations in protein engineering allow us to use light to directly activate specific neurons in the larva. For instance, we can engineer larvae with light-activable neurons in their “noses.” When presented with red light, these larvae perceive an odor and respond by attempting to find its source. Using sophisticated light patterns and analysis methods, we developed an assay that allowed us to quantify how the larva makes decisions based on multiple sources of sometimes conflicting information.

Advances similar to the ones that allow us to activate neurons using light allow us to measure thought patterns using light microscopes. Because the larva is almost clear, it has been a long-standing goal to use a microscope to “read the larva’s mind” as it navigates its surroundings. However, the 3D brain movements generated by the larva’s complicated locomotion have prevented optical recording of neural activity in behaving larvae. We developed a two-photon microscope capable of tracking single neurons moving rapidly in 3D while monitoring their activity in real time without motion artifacts. To record from many neurons we added a second beam that scans the volume around the tracked neuron to enable motion-corrected volumetric imaging in a freely-behaving animal. This allowed us to image correlated activity of motor and pre-motor neurons from a significant portion of larva’s “spine” in a completely unrestrained crawling animal. I will use these techniques to follow information flow through the larva’s circuits during sensory-motor transformations and achieve a neuron-level understanding of how a simple brain implements fairly complex calculations.

This event was first published on January 27, 2020 and last updated on January 28, 2020.


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