The function of a brain region is an emergent property of many cell types. The criteria needed to understand a network have been established in studies of invertebrate """"""""simple"""""""" networks, but there has not yet been an attempt to provide such a full, mechanistic understanding of any network in the vertebrate brain. We believe that the time is now ripe for such an effort. Specifically, we propose to understand how the CA3 network in the hippocampus generates sharp-wave-ripples (SWR). These events are of great interest because of their cognitive function: they represent replay of episodic memory sequences and are required for subsequent memory recall, as demonstrated at the behavioral level. Our efforts to understand the SWR will build on previous work establishing the cell types of the hippocampus. However, to meet the criteria for """"""""understanding"""""""", a great deal of additional information about connectivity and intrinsic properties of cells must be obtained. We will use recently developed large-scale electrical and optical recording methods and ontogenetic to obtain this information. In addition, several new methods/tools will be developed. Notably, we propose to optimize a novel synapse localization optical method to obtain high-throughput cell type-specific information about the connective of the CA3 network. We will also construct the first full-scale computational model of the CA3 region of the hippocampus, in which every cell and synaptic connection is explicitly represented. This strictly data-driven, full-scale model will provide a widely applicable tool for synthesizing experimental results and testing our ability to understand the principles that underlie SWR generation.
We propose to make the first attempt to fully understand a cognitively important event, called memory replay, in terms of the detailed properties of the brain cells involved. We will use cutting-edge large-scale recording technologies to study and manipulate identified cell types, and develop a novel method that will provide needed information about the connectivity between the neurons involved. Finally, we will construct the first full- scale computational of model of the brain area that produces the memory replay in which every cell is explicitly simulated. These powerful new approaches are likely to yield major insights into the principles by which the interactions of neurons gives rise to cognitive function, with important implications for memory disorders.
|Danielson, Nathan B; Kaifosh, Patrick; Zaremba, Jeffrey D et al. (2016) Distinct Contribution of Adult-Born Hippocampal Granule Cells to Context Encoding. Neuron 90:101-12|
|Watson, Brendon O; Levenstein, Daniel; Greene, J Palmer et al. (2016) Network Homeostasis and State Dynamics of Neocortical Sleep. Neuron 90:839-52|
|Danielson, Nathan B; Zaremba, Jeffrey D; Kaifosh, Patrick et al. (2016) Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1. Neuron 91:652-65|
|Kaifosh, Patrick; Losonczy, Attila (2016) Mnemonic Functions for Nonlinear Dendritic Integration in Hippocampal Pyramidal Circuits. Neuron 90:622-34|
|Krook-Magnuson, Esther; Gelinas, Jennifer N; Soltesz, Ivan et al. (2015) Neuroelectronics and Biooptics: Closed-Loop Technologies in Neurological Disorders. JAMA Neurol 72:823-9|
|BuzsÃ¡ki, GyÃ¶rgy (2015) Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 25:1073-188|
|Kabakov, Anatoli Y; Lisman, John E (2015) Catalytically Dead Î±CaMKII K42M Mutant Acts as a Dominant Negative in the Control of Synaptic Strength. PLoS One 10:e0123718|
|Lisman, John (2015) The challenge of understanding the brain: where we stand in 2015. Neuron 86:864-82|
|Sanders, Honi; RennÃ³-Costa, CÃ©sar; Idiart, Marco et al. (2015) Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function. Trends Neurosci 38:763-75|