Achievement

Sonya Giridhar is a Biological Sciences student investigating how recruitment of inhibitory interneurons (granule cells) in the olfactory bulb modifies correlations between pairs of mitral cells over a broad range of timescales. With no previous computational background, she proposed an IGERT project to construct a modest two-cell model to study the impact of inhibition on firing correlations across timescales. Giridhar took to modeling right away, and was able to complete her initial model during the first year of her two-year project. So she decided to expand her model to include hundreds of neurons, and added an explicit model of interneurons in place of an abstract Poisson process. This new model has allowed her to look at population coding implications of the correlation effect she studies. She has shown that the simultaneous fast correlation (synchrony) and slow decorrelation (rate separation) observed in experiments can function to improve odor discrimination of the network.