The Study of Neural Circuits and Behavior in C. Elegans Using Biophysical Tools

When: Tuesday, March 29, 2011 at 4:00 pm
Where: DA 114
Speaker: Heonick Ha
Organization: Harvard University
Sponsor: Joint CIRCS and Physics Colloqium

A central goal of neuroscience is to understand how dynamics of neural circuits generate behavioral output. However as this demands not only molecular, cellular and genetic tools but also biophysical methods that can accurately quantify its behaviors and manipulate single level of neurons in a circuit. Moreover the difficulty also lies in the complexity of neural networks as human brains contain billions of neurons and trillions of synapses.

The simple model organism, Caenorhabditis elegans, has small nervous system (only 302 neurons), and the well-defined anatomy of this network can allow neural circuit to be dissected at the level of individual neurons to approach   fundamental biological questions.

One remarkable behavior in C. elegans is after experiencing an olfactory cue concurrent with harmful experience, animals learn to avoid the odour. In this talk, I will show that how the naïve and experience-dependent olfactory preferences can both be encoded in a neural network, as well as how these alternative preferences switch with each other in a systems-level analysis.  We have mapped an olfactory neural network, from sensory neurons to motor neurons, that regulates the ability of Caenorhabditis elegans to learn to avoid the smell of pathogenic bacteria, a potential peril in their food source.  We used laser ablation of individual neurons and a novel single-animal learning assay to map two anatomically distinct and interconnected neural circuits that regulate these preferences. And also using behavioral analyses and calcium imaging recordings, we find that the intrinsic properties of olfactory sensory neurons encode the naïve preference and the sensory response is transduced into locomotory response through a downstream sensorimotor circuit. These results reveal the functional architecture of an olfactory neural network that allows animals to display the naïve olfactory preference as well as experience-dependent plasticity under different conditions.