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 functions.br / 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 | ||
| Research Project | |||
| Research Team | |||
More like this
Provide the latest information of AI research centers and applied industries
-
AIoT Aquaculture Technology Co., Ltd.
Artificial Intelligence Techniques Aquaculture Management System: The system uses the omni-IoT system to collect big data, provides AI algorithm for each module, included AI feeding module, fish body length weight measurement module, smart submersible cage module, and provides better fish growth control , to reduce residual bait, to improve survival rate of fish, to save manpower, to reduce the threshold, cost and risk of smart cage culture operation. The AIoT system of our team is mainly self-made, which greatly saves costs and is modularized. Aquaculturist can choose modules to use allow young fishermen to profit easily even if they do not have a lot of farming experience.
-
A movement assistive system using EEG-controlled functional electric stimulation
We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network, and the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG system and our own FES system.
-
Sampling and Prediction of Photolithography Process and Applications of Incremental Learning Model
Through the technique, Sampling and Prediction of Lithography Overlay Errors, the cost and time of overlay error measurement can be reduced to improve the process efficiency. We identify key sampling through clustering and machine learning models, and design a new sampling algorithm in photolithography process. Due to the complexity of wafer and many training factors of wafer data, we combine the clustering algorithm with incremental learning to meet customers＇ unique needs and achieve the goal of optimally sampling and reducing the costs.
-
Highly Efficient Quantum Key Distribution System
div style="text-align: justify"Quantum key distribution exploits the transmissiondetection of single photons’ quantum states to generatedistribute secure keys, allowing absolutely secure communication. We use a self-developed miniature single-photon source, together with the single-photon wavepacket engineering, to implement a highly efficient protocol of differential-phase-shift quantum key distribution. Using the Campus Fiber Network between National Tsing Hua UniversityNational Chiao Tung University, we demonstrate Taiwan’s first outdoor quantum key distribution. The technology not only benefits the development of distance unlimited absolutely secure communication networks for the commercialmilitary uses, but also opens up new opportunities for the R&Dmarkets in industry./div
-
Shiann-Rong Kuang《AI Technology may be the key to reviving the Shrimping Industry》
div style="text-align: justify"Taiwan was once the shrimp capital of the world. Though just a small island, the technology, abundance,reputation was globally known. Over time, the changes in water qualitythe environment led to lessless shrimp. AI may be the innovative solution needed to revive the industry./div
-
Chung-Cheng Chang《AI Technology: Fishery Super Upgrade》
Taiwan was formerly the Shrimp Capital of the World. With the changing of the timesincreasingly updated technology, AI smart farming is the new milestone for Taiwan’s agriculturefisheries.
-
Integration of an ICU Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds
Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). The surgical ICU team of National Cheng Kung University Hospital has developed and integrated i-Dashboard as a platform to facilitate MDRs. i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touchscreens during MDRs. The i-Dashboard increases the efficiency in data gathering and enhances communication accuracy and information exchange in MDRs.
-
Super-fast Convergence for Radiance Fields Reconstruction
The NeRF-based technique describes a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.
-
Machine Learning to Predict In-Hospital Cardiac Arrest in Patients Admitted from the Emergency Department with COVID-19 and Suspected Pneumonia
By using the machine learning algorithms, this study developed a risk stratification model for predicting the occurrence of in-hospital cardiac arrest (IHCA) events in patients admitted from the emergency department with COVID-19 and pneumonia. The results showed that the model's performance is better than by using the National Early Warning Score (NEWS).
-
Cardiovascular Health Guardian – Novel Pulse Wave Velocity and Personal Blood Pressure Estimation System for Smart Watch
Our team develops an accurate PWV estimation algorithm that uses wrist PPG and ECG signals from wearable devices. A missing-feature imputation and ambiguous-feature resolution technique is developed and the availability of wrist PPG morphological features is raised from 60% to 99.1%. A weighted pulse decomposition approach is adopted and 5 component waves can be acquired to examine more detailed properties. The PWV is then estimated by XGBoost algorithm with the hierarchical regression model.
