Active Genetics & Molecular Biology Brain & Nervous System
Discovering the circuit and molecular basis of inter-strain and inter-species differences in learning
Summary
Original abstract (not yet simplified)Across the animal kingdom, closely-related species can differ in their learning ability, as can differentindividuals of the same species, but the underlying causes of these differences are poorly understood.Differences in learning could be due to different structural and functional properties of learning circuits whichin turn result from different patterns of gene expression. However, identifying homologous learning circuitneurons in different species...
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Across the animal kingdom, closely-related species can differ in their learning ability, as can differentindividuals of the same species, but the underlying causes of these differences are poorly understood.Differences in learning could be due to different structural and functional properties of learning circuits whichin turn result from different patterns of gene expression. However, identifying homologous learning circuitneurons in different species for comparing their synaptic connectivity, cellular-resolution functional propertiesand transcriptomes is challenging in larger brains.We have identified a strain of Drosophila melanogaster larvae that learns faster (after fewer training trials)than others and a closely related Drosophilid species that learns faster than the D. melanogaster strains. Wewill use this tractable genetic model system to compare learning circuits in faster/better and slower/worselearnerstrains and species, in particular, their synaptic resolution connectomes; cellular-resolution activitymaps; and transcriptomes. We will then screen the differentially expressed genes to identify those that improvelearning and determine the way in which they affect connectivity and functional properties of learning circuits.This project will reveal the architectural features of learning circuits that enhance learning and memory, aswell as the molecules that can transform slow/worse- into fast/better-learners and their mechanisms of action.Uncovering the underlying structural, functional and genetic causes of variability in learning will not onlyhave a major impact on neuroscience but could also potentially inspire the development of better architecturesand algorithms for artificial intelligence and provide new avenues for treating learning and memory deficits.
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