New insight into the brain: Optical imaging/stimulationspiking neural circuit models
Summary div style="text-align: justify"Constructing a functional connectomeits computational model is a crucial step toward understanding the mechanisms of brain functions. To achieve this goal, we developed two correlated technologies: (1) An all- optical physiology (AOP) that is capable of millisecond volumetric imagingaccurate stimulation in living animal brains. This system allows us to establish functional connectomeneural coding with a single-cell resolution. (2) A cellular-level spiking neural circuit simulation system that is capable of tuning itself based on the input data from the AOP system. We have demonstrated our technologies in the Drosophila late visual systemwill apply them in the brains of larger species such as mice. We expect that our technologies will be able to greatly enhance our knowledge of the brain operation principles. Our 3D all-optical physiology (AOP) platform incorporates single-photon point stimulationtwo-photon high-speed volumetric recordings (Optics Letters 2019, "Editors pick"). We have demonstrated its effectiveness in studying the anterior visual pathway of fruit flies (iScience2019). In comparison, contemporary high-speed AOP platforms are limited to single-depthdiscrete multi-plane recordings that are not suitable for studying functional connections. Our high-resolution computational model is constructed based on the combination of static connectomeAOP data,is much more realistic than the existing models. Our work aids establishing in-vivo 3D functional connectomescomputational models of the brains, thus provides insight into the mechanisms of brain /
Our fastvolumetric all-optical physiology platform can be applied not only to neuroscience for various species, but also to other fast dynamical systems. For example, the platform can improve the effectiveness of cell screening in microfluid devices by accurately observingactivating individual cells. The platform also has potential applications in pathological diagnosis of tumor samples. Our algorithms for constructingtuning neural circuit models based on observed neural signals may be used in brain-machine interfacesmay assist in the development of the next-generation AI algorithms./div
Keyword Brain Science Brain-Computer Interface Cranial Nerve Computation Model Neural Network Computation Model
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