YR 1-2: Collaborative Research: Dynamic and Distributed Memory in Olfaction

Project: Research project

Project Details

Description

A combined experimental-computational approach will be used to understand the differences between unsupervised and supervised learning using olfactory processing as a model system. It is becoming increasingly clear that different forms of plasticity are distributed across different layers of processing during olfactory learning. The honey bee will be used as a model for studying these different forms of plasticity in behavioral experiments involving different kinds of conditioning experience combined with simultaneous multiunit recordings of the outputs of two sequential layers of olfactory processing the Antennal Lobe and Mushroom Body. Combining behavior with electrophysiology in the same animals allows for investigation of how different forms of plasticity interact with the transformation of spatiotemporal transient activity patterns in the AL to sparse, spatial representations in the MB. Our group also has developed models of the AL and MB that produce realistic spiking activity patterns. In the proposal these models will continue to be extended to investigate how unsupervised and supervised (reinforcement-based) learning may be implemented in the AL and MB. For example, recent immunohistochemical analyses by one of our laboratories of the reinforcement pathway in the honey bee brain have revealed that reinforcement targets inhibitory pathways both in the AL and MB. These inhibitory pathways are important for generating transient activity patterns in the AL and the sparse coding in the MB. This information will be used to implement plasticity in the computational models. Although these models provide predictions and guidance they are still far from a clear convergence with electrophysiological and behavioral data. The work proposed in this proposal is aimed toward development of an integrative view in which new data will refine development of the models, and the models will help to guide experimental approaches. For example, spike time dependent plasticity and Hebbian learning are dominant when there is no reward and non-supervised learning occurs. On the other hand, in the honey bee octopamine release from a well-established reward pathway is critical for reinforcement learning. Our preliminary models show that Hebbian type of learning increases the similarity of correlated patterns while it separates dissimilar patterns. On the other hand, reinforcement learning operates against the Hebbian-type of rules by increasing correlations between dissimilar patterns or separating highly correlated encoded signals. The current proposal focuses on empirically and computationally testing what underlying biophysical mechanisms can lead to such observations.hdnLi
StatusFinished
Effective start/end date7/1/106/30/15

Funding

  • HHS: National Institutes of Health (NIH): $663,111.00

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