Electrochemical imaging of quantal exocytosis in single cells

Microelectrodes are widely used in detection of exocytosis events. In order to detect both the time and release location of quantal exocytosis from a single cell, four square microelectrodes located in a 20 um square microwell were fabricated through hotolithographic techniques. A 30 nm thin gold films were used as the material for the microelectrodes and the microwell was fabricated using SU8 thick photoresist. In order to test the quality of microelectrodes, we used cyclic voltammetry technique and the test analyte ferricyanide prior to the amperometry recording. A high density of chromaffin cells were placed in the solution reservoir on top of the electrode arrays, and individual cells were targeted in to the microwells automatically. Poly (L-lysine) was coated on the microelectrode to promote the cells adhesion. Following of that, exocytosis events were triggered by introducing a high potassium concentration to the bath solution. The data obtained from cell recordings were compared with the simulation data obtained from FEM modeling and the locations of release sites were identified. It was observed only sites of quantal releases with relatively high amount of charge (3.59+0.58 pC) can be identified. In order to expand the area on the cell in which the electrochemical imaging is attainable, a simulation-guided electrode re-design was tested using FEM simulations. The results from simulations showed that the improved design, with curved-like electrodes, is predicted to increase the area of detection by approximately 45% compared to the design used in cell tests

"Task-relevant autoencoding" enhances machine learning for human neuroscience

In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior. We thus developed a Task-Relevant Autoencoder via Classifier Enhancement (TRACE), and tested its ability to extract behaviorally-relevant, separable representations compared to a standard autoencoder, a variational autoencoder, and principal component analysis for two severely truncated machine learning datasets. We then evaluated all models on fMRI data from 59 subjects who observed animals and objects. TRACE outperformed all models nearly unilaterally, showing up to 12% increased classification accuracy and up to 56% improvement in discovering "cleaner", task-relevant representations. These results showcase TRACE's potential for a wide variety of data related to human behavior.Comment: 41 pages, 11 figures, 5 tables including supplemental materia

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function