Browsing by Author "Chu, Joshua P."

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    GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data
    (Society for Neuroscience, 2021) Chu, Joshua P.; Kemere, Caleb T.
    Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time–frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.
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    Realtime decoding with state space models of neural activity
    (2023-10-06) Chu, Joshua P.; Kemere, Caleb T.
    Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments. Here we present a Python software system that implements online clusterless decoding, using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency < 50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripple and multiunit population burst events. Compared against state-of-the-art solutions which use compiled programming languages, performance is not significantly impacted. Next, we demonstrate its use in a live rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoding hippocampal activity. Early results indicate the possibility of strengthening neural representations ("replay") of spatial locations experienced in the real world.