iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
Authors: Marine Schimel, Ta-Chu Kao, Kristopher T Jensen, Guillaume Hennequin
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of i LQRVAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that i LQR-VAE yields high-quality kinematic reconstructions from the neural data. |
| Researcher Affiliation | Academia | Marine Schimel Department of Engineering University of Cambridge Cambridge, UK mmcs3@cam.ac.uk Ta-Chu Kao Gatsby Computational Neuroscience Unit University College London London, UK c.kao@ucl.ac.uk Kristopher T. Jensen Department of Engineering University of Cambridge Cambridge, UK ktj21@cam.ac.uk Guillaume Hennequin Department of Engineering University of Cambridge Cambridge, UK g.hennequin@eng.cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 i LQRsolve(Cθ(u), uinit)) |
| Open Source Code | No | The paper mentions using a third-party LFADS implementation: 'We used the LFADS implementation from https://github.com/google-research/ computation-thru-dynamics/tree/master/lfads_tutorial, which we modified to include linear dynamics and Gaussian likelihoods.' However, there is no explicit statement or link indicating that the authors' own code for iLQR-VAE is open-source or publicly available. |
| Open Datasets | Yes | To allow for direct comparison with benchmarks reported Pei et al. (2021), we first used data provided by the Neural Latents Benchmark (NLB) challenge, available at https://gui. dandiarchive.org/#/dandiset/000128. |
| Dataset Splits | Yes | We used 1720 training trials and 510 validation trials, which were drawn randomly for each instantiation of the model to avoid overfitting to test data. |
| Hardware Specification | No | Averaging over data samples can be easily parallelized; we do this here using the MPI library and a local CPU cluster. This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. While CPUs are mentioned, no specific models or detailed specifications are provided for the cluster hardware. |
| Software Dependencies | No | We optimize the ELBO using Adam (Kingma and Ba, 2014) with a decaying learning rate 1/ i where i is the iteration number. Averaging over data samples can be easily parallelized; we do this here using the MPI library and a local CPU cluster. Neither Adam nor MPI library versions are specified. |
| Experiment Setup | Yes | We optimized the ELBO using Adam (Kingma and Ba, 2014) with a decaying learning rate 1/ i where i is the iteration number. We optimized the model parameters with Adam, using (manually optimized) learning rates of 0.04/(1+ p k/1) for the free i LQR-VAE model, 0.04/(1+ p k/1 for autonomous i LQR-VAE and 0.02/(1+ p k/30 for LFADS, where k is the iteration number. For this experiment, we fitted i LQR-VAE to the neural activity using a model with MGU dynamics (n = 60), a Student prior over inputs (m = 15), and a Poisson likelihood (no = 182 neurons). |