Recent advancements in the ability to measure and manipulate large-scale brain activity with high resolution have significantly enhanced our understanding of the coordination of brain-wide activity, a crucial aspect of brain computation. My project develops a novel system for simultaneously recording and inducing brain activity in mice, using calcium imaging and optogenetics to measure and manipulate brain activity, respectively. This system creates a more streamlined approach to measure activity across the entire cortex against stimuli and behavior, enabling comprehensive study into the mechanisms of inter-area brain activity. The experiments are done with conscious mice in a setup that includes custom software and hardware control using MATLAB, and data is analyzed using Python. One challenge is that the red optogenetic laser can cause aberrant visual responses. When the laser is near the retina, some of the light can travel through neural tissue and hit retinal neurons, ultimately starting a neuronal signaling cascade. An aberrant visual response can interfere with optogenetic effects. I have determined that laser powers greater than 1 mW can elicit this off-target visual response and that we can diminish the response to the laser using a noisy, flickering visual stimulus. When the flickering stimulus is present, the laser power needed to cause a visual response is increased by 100%. In the future, we will use the combination of techniques to understand what brain-wide mechanisms underlie goal-directed behaviors. By measuring and manipulating cortex-wide activity while a mouse completes a task, we can investigate communication between different parts of the brain and identify the mechanisms that impact learning. This technique has implications in more advanced studies of synaptic plasticity, computational modeling, and brain-wide cognition, offering promising new avenues for neuroscience research in the future.

Achieving consistent targeting of multiple simultaneous probes during electrophysiology experiments is a challenging and time-consuming process. Even with a planned insertion trajectory, experimenters still have to go through a lengthy process of positioning and inserting each probe. Electrophysiology experiments are increasingly focused on brain-wide coverage, requiring three or more simultaneous probes motivating researchers to accelerate their processes to reduce the duration of the experiment and the corresponding stress levels of their subjects. To improve the efficiency and reproducibility of multi-probe electrophysiology experiments, we developed two frameworks: a communication platform to allow software control of hardware micro-manipulators and an automation platform to perform multiple synchronous probe insertions. Each existing manipulator platform has proprietary software for programmatic control, which is rarely cross-platform and often exposes inconsistent interfaces. To standardize manipulator communication, we developed a Python server that acts as a generic cross-platform application programming interface (API). This platform ensures that client applications only need to interface with one API to be compatible with many different manipulator platforms connected across various computer operating systems. Building on top of this communication platform and an existing trajectory planning tool, Pinpoint, we next developed a system that automates the insertion process for multiple probes, saving time. The automation system provides three guarantees for researchers: first, that probes will reach their intended targets without manually introduced errors in targeting; second, that experiments can be repeated exactly to improve reproducibility; and third, that movement speeds are limited to low levels for reduced tissue damage. Because our software drives multiple probes simultaneously, complex multi-probe insertions are more manageable. Taken together, these open-source tools for communicating with hardware manipulators and automating multi-probe insertions enable the next generation of reproducible, high-efficiency, brain-wide electrophysiology data collection.

Typical data visualizations in neuroscience flatten 3D space into just two dimensions, limiting researchers ability to observe spatial relationships. To overcome this limitation, we have previously developed rendering tools to support exploratory 3D visualizations, specifically for neuroscience data. In this project, I am expanding the renderer to allow users to display and explore additional non-spatial dimensions of their data. These new tools will allow users to explore additional dimensions of their dataset such as time, stimulus properties, or the spatial position of an animal. For example, to explore time, I have developed an interactive slider bar that dynamically updates the 3D display and a corresponding linked 2D plot, providing a clear depiction of neural activity with relation to specific events. Scrolling along the 2D plot enables users to pinpoint their position in time relative to stimulus onset, with the 3D display concurrently adjusting to reflect the data from that specific snapshot in time. These functions are packaged into the API of the renderer, streamlining the process for users to transform raw data into intuitive and interactive visualizations. Reducing the complexity of the code expands the accessibility of these new features, making them more approachable for new users who may be less familiar with coding. By supporting additional dimensions, users will be able to develop visualizations that are tailored to their individual research projects. My objective is to create research tools that are versatile, applicable to a range of projects, and accessible to individuals with diverse levels of experience, including students and researchers of varying programming backgrounds.