Dissecting the Role of Dendrites in Memory (dEMORY)
Dissecting the Role of Dendrites in Memory
Start date: Oct 1, 2012,
End date: Sep 30, 2017
Understanding the rules and mechanisms underlying memory formation, storage and retrieval is a grand challenge in neuroscience. In light of cumulating evidence regarding non-linear dendritic events (dendritic-spikes, branch strength potentiation, temporal sequence detection etc) together with activity-dependent rewiring of the connection matrix, the classical notion of information storage via Hebbian-like changes in synaptic connections is inadequate. While more recent plasticity theories consider non-linear dendritic properties, a unifying theory of how dendrites are utilized to achieve memory coding, storing and/or retrieval is cruelly missing. Using computational models, we will simulate memory processes in three key brain regions: the hippocampus, the amygdala and the prefrontal cortex. Models will incorporate biologically constrained dendrites and state-of-the-art plasticity rules and will span different levels of abstraction, ranging from detailed biophysical single neurons and circuits to integrate-and-fire networks and abstract theoretical models. Our main goal is to dissect the role of dendrites in information processing and storage across the three different regions by systematically altering their anatomical, biophysical and plasticity properties. Findings will further our understanding of the fundamental computations supported by these structures and how these computations, reinforced by plasticity mechanisms, sub-serve memory formation and associated dysfunctions, thus opening new avenues for hypothesis driven experimentation and development of novel treatments for memory-related diseases. Identification of dendrites as the key processing units across brain regions and complexity levels will lay the foundations for a new era in computational and experimental neuroscience and serve as the basis for groundbreaking advances in the robotics and artificial intelligence fields while also having a large impact on the machine learning community.
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