Open Science
Science is not just the end product of the scientific process, but a whole ecosystem of knowledge - methodology, data, tools. Freely and openly sharing these resources not just increases scientific reproducibility and collective understanding, but is a practice of inclusivity, sustainability, and fairness.
Please refer to the Publications page for access to pdf+data/code of peer-reviewed articles.
Code
- Modeling recurrent networks with feedback controllers + adaptation [code] [paper]
- Python code for internal model estimation from joint neural-behavioral data [code] - based off Matlab code by Matt Golub
- Fitting GLM-HMM models to behavioral data [code]
- Model predictive control for embodied simulations in Mujoco - of humanoids + Janelia Drosophila model [code]
- Silverlab imaging acquisition software - For AOL-based scanning and pointing [code]
- Preprocessing of calcium imaging data (Silverlab AOL-RAM scope) [code: GoCs] [code2]
- GoC Network Model in Gurnani and Silver, 2021 [code]
- Basic network model for electrically-coupled GoCs [code]
- Protocols for Reward Conditioning task [code]
External resources (that I found useful)
- NeuroML: A Standard framework for neuroscience model descriptions + integrated use of multiple simulators [paper] [link] [code]
See here for python package installation and usage. - PyDMD: Dynamic mode decomposition for dynamics estimation [code]
- PyKalman: Kalman filter library [code]
- FixedPointFinder: Fixed point finder for Pytorch and Tensorflow network models (supports a limited class of model units) [code]