Projects¶
Code for most of my projects can be found on my GitHub page. Here are some select projects I’ve worked on:
Featured¶
Spyglass is a data analysis framework that facilitates the storage, analysis, visualization, and sharing of neuroscience data to support reproducible research. It is designed to be interoperable with the NWB format (a data standard for neurophysiology) and integrates open-source tools such as SpikeInterface and DeepLabCut into a coherent framework. I am a co-first author on the eLife reviewed preprint.
Python NWB Data Framework
replay_trajectory_classification is a Python package for decoding spatial position represented by neural activity and categorizing the type of trajectory. It can decode both clustered and unclustered spiking activity and uses GPU acceleration for fast inference. The underlying state-space methods are described in our eLife paper.
Python GPU Neuroscience
More projects¶
spectral_connectivity computes multitaper spectral estimates and frequency-domain brain connectivity measures (coherence, spectral Granger causality, phase-lag index) on the CPU and GPU. Published in JOSS.
Python Signal Processing Neuroscience
track_linearization projects 2D animal-tracking position to 1D for any spatial environment, with visualization tools for inspecting the projection. The hippocampal field often relies on this transformation to simplify downstream analyses.
Python Spatial Analysis Neuroscience
ripple_detection is a Python package collecting the Frank lab methods for detecting hippocampal sharp-wave ripples (150-250 Hz oscillations associated with replay), so the procedure is transparent and reproducible across labs (see Liu et al. 2022 for a comparison).
Python Signal Processing Neuroscience
