Bayesian target optimisation for high-precision holographic optogenetics
Authors: Marcus Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. |
| Researcher Affiliation | Academia | 1Department of Statistics, Columbia University 2Zuckerman Mind Brain Behavior Institute, Columbia University 3Department of Molecular and Cell Biology, UC Berkeley |
| Pseudocode | Yes | Algorithm 1: Bayesian target optimisation (Bataro).; Algorithm 2: Projected Monte Carlo gradient descent algorithm for optimising single-target holograms |
| Open Source Code | Yes | An open-source implementation of Bayesian target optimisation is available in Python at https: //github.com/marcustriplett/bataro. |
| Open Datasets | No | The paper mentions using 'detailed cell-attached recordings in slice [42]' to create synthetic optogenetics experiments, but reference [42] is another paper (bioRxiv), not a direct link or citation to a publicly available dataset. |
| Dataset Splits | Yes | We selected the parameters of the GP covariance kernel using 5-fold cross-validation on a separate set of recordings that were made on the same set of four cells, ensuring the hyperparameter selection was using out-of-sample data... For each hyperparameter combination θ and for each cell, we used Newton s method to fit the GP-Bernoulli model to 80% of the trials... On the remaining 20% of the trials (denoted as Theld-out), we evaluated the log-likelihood |
| Hardware Specification | Yes | All computational procedures were performed either on a desktop workstation running Ubuntu 18.04 with an Intel Xeon E5-2620 v4 CPU, four GTX 1080 Ti GPUs, and 112GB RAM, or on the Axon computer cluster based at the Zuckerman Institute (Columbia University) using nodes comprised of two Xeon E5-2660 v4 CPUs, eight GTX 1080 Ti GPUs, and 125GB RAM. |
| Software Dependencies | No | The paper mentions 'Ubuntu 18.04' which is an operating system, and 'Python' for implementation, but does not provide specific version numbers for other key software components, libraries, or frameworks used (e.g., PyTorch, NumPy). |
| Experiment Setup | Yes | Table S1: Parameters used for simulations and generating synthetic optogenetics experiments. This table provides specific hyperparameters such as 'Kernel radial lengthscale', 'Kernel power lengthscale', 'Kernel amplitude', and 'Learning rate for spike thresholds'. |