Animal learning is thought to involve the precise re-wiring of neuronal circuitry so as to encode novel information, facilitating everything from the learning of a sensory stimulus to the development of a motor skill. While considerable effort has been placed into identifying the specifics of the circuit rearrangements underlying animal learning, the complexity of neural tissue makes the scope of this task a daunting one. This challenge is compounded by the simultaneous need for functional information about how the activity of specific connections relates to the learned behavior. Ultimately, the ideal platform to overcome this sizeable obstacle would involve the study of a minimal ?unit? of neuronal computation, where the collective of connectivity changes occurring over learning can be understood as contributing to the function of this unit. Because of their unique role as computationally semi-isolated integrative compartments, neuronal dendrites are well suited to this demand. Further, many lines of evidence now support small groups of synapses on individual dendrites ? so-called synaptic ?clusters? ? as disproportionate contributors to the function of dendrites and, therefore, to their parent neurons. Thus, the study of learning-related synaptic clusters is an ideal starting point for extracting meaningful information about impactful connectivity changes occurring over learning. The current research seeks to use a novel combination of imaging and molecular approaches to reconstruct the circuit details of learning-related synaptic clusters. Specifically, this work will capture both the functional activity of synapses ? especially those in clusters ? across the length of dendrites as they relate to behavior and identify the brain regions of origin of the inputs impinging on these synapses. These efforts will thus permit a reconstruction of the partial ?connectome? of individual dendrites, with simultaneous information about how the different connections interact with each other (i.e. the coherence of their activity) in the integrative compartment of the dendrite. Such results will have strong implications for how different information streams are integrated at the level of dendrites, and further, will reveal how these integration schemes are sculpted by learning. By using a well characterized model of learning in the form of a simple motor learning task, this work will additionally allow a quantitative description of how specific learning-related connectivity changes are associated with a learned behavior. This work will thus provide an unprecedented level of detail about specific patterns of connectivity changes that occur over learning and will give insights into how such patterns inform learned neuronal activity and, in turn, a learned behavior. These efforts will provide a critical tool set to the neuroscience community and will establish a detailed framework in which diseases affecting neuronal connectivity can be understood.
Understanding the specific circuit rearrangements that occur when an animal learns is of central importance to the field of neuroscience and will allow for a better contextualization of diseases affecting learning. However, the staggering density of neural tissue makes the specific identification of learning-related connectivity changes prohibitively difficult. The proposed research seeks to use a novel combination of cutting-edge imaging and molecular tools to identify the specific circuit details of learning-related synapses, including the identities of synaptic inputs, allowing a detailed description of how information from different brain regions converge to encode new information over learning.
