Latent factor analysis via dynamical systems (LFADS) is a variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional neural spiking activity for downstream applications in science and engineering [1, 2, 3, 4]. Recently introduced variants have continued to demonstrate the applicability of the architecture to a wide variety of problems in neuroscience [5, 6, 7, 8]. Since the development of the original implementation of LFADS, new technologies have emerged that use dynamic computation graphs [9], minimize boilerplate code [10], compose model configuration files [11], and simplify large-scale training [12]. Building on these modern Python libraries, we introduce lfads-torch — a new open-source implementation of LFADS designed to be easier to understand, configure, and extend.

To create an environment and install the dependencies of the project, run the following commands:

git clone https://github.com/arsedler9/lfads-torch.git
conda create --name lfads-torch python=3.9
conda activate lfads-torch
cd lfads-torch
pip install -e .
pre-commit install

The first step in applying lfads-torch to your dataset is to prepare your preprocessed data files. We recommend saving your data as n_samples x n_timesteps x n_channels arrays in the HDF5 format using the following keys:

• train_encod_data: Data to be used as input when training the model.
• train_recon_data: Data to be used as a reconstruction target when training the model.
• valid_encod_data: Data to be used as input when validating the model.
• valid_recon_data: Data to be used as a reconstruction target when validating the model.

Note that for both training and validation data, encod_data may be the same as recon_data, but they can be different to allow prediction of held out neurons or time steps.

Create a new configuration file for your dataset (e.g. configs/datamodule/my_datamodule.yaml). Note that datafile_pattern can be a glob-style pattern to accommodate multisession runs, but may only match a single file.

_target_: lfads_torch.datamodules.BasicDataModule
datafile_pattern: <PATH-TO-HDF5-FILE>
batch_size: <YOUR-BATCH-SIZE>

We also provide preprocessed example data files from the Neural Latents Benchmark in datasets. With nlb_tools installed in your environment, you can additionally use the NLBEvaluation extension to monitor NLB metrics while training lfads-torch models.

Next, you'll need to create a model configuration file that defines the architecture of your LFADS model (e.g. configs/model/my_model.yaml). We recommend starting with a copy of the configs/model/nlb_mc_maze.yaml file. At the least, you'll need to specify the following values in this file with the parameters of your dataset:

• encod_data_dim: The n_channels dimension of encod_data from your data file.
• encod_seq_len: The n_timesteps dimension of encod_data from your data file.
• recon_seq_len: The n_timesteps dimension of recon_data from your data file.
• readout.modules.0.out_features: The n_channels dimension of recon_data from your data file.

While this is an easy way to get up and running with LFADS relatively quickly, these default hyperparameters are unlikely to be the best ones for your particular dataset. We recommend sweeping over architecture and regularization hyperparameters in order to maximize performance.

To train a single model on your dataset, start with the scripts/run_single.py script. Edit the path specified by RUN_DIR to your desired model directory and edit the overrides argument to run_model to the following:

overrides={
    "datamodule": "my_datamodule",
    "model": "my_model",
}

This will tell lfads-torch to use the custom datamodule and model configurations you just defined. Running this script in your lfads-torch environment should begin optimizing a single model on your GPU if it is available. Logs and checkpoints will be saved in the model directory, and model outputs will be saved in lfads_output_sess{i}.h5 when training is complete. Feel free to inspect configs/single.yaml and configs/callbacks to experiment with different Trainer arguments, alternative loggers and callbacks, and more.

As a next step, try specifying a random search in scripts/run_multi.py or a population-based training run in scripts/run_pbt.py and running a large-scale sweep to identify high-performing hyperparameters for your dataset!

lfads-torch is designed to be modular in order to easily adapt to a wide variety of use cases. At the core of this modularity is the use of composable configuration files via Hydra [11]. User-defined objects can be substituted anywhere in the framework by simply modifying a _target_ key and its associated arguments. In this section, we draw attention to some of the more significant opportunities that this modularity provides.

Reconstruction losses play a key role in enabling LFADS to model the data distribution across data modalities. In the original model, a Poisson reconstruction trains the model to infer firing rates underlying binned spike counts. In subsequent extensions of LFADS, Gamma and Zero-Inflated Gamma distributions have been used to model EMG and calcium imaging datasets [5, 6]. In lfads-torch, we provide implementations of Poisson, Gaussian, Gamma, and Zero-Inflated Gamma reconstruction costs and allow the user to easily define their own reconstructions. These can be easily selected using the reconstruction argument in the model configuration.

Data augmentation is a particularly effective tool for training LFADS models. In particular, augmentations that act on both the input data and the reconstruction cost gradients have endowed the model with resistance to identity overfitting (coordinated dropout [3, 4]) and the ability to infer firing rates with spatiotemporal superresolution (selective backpropagation through time [6, 7]). Other augmentations can be used to reduce the impact of correlated noise [5]. In lfads-torch, we provide a simple interface for applying data augmentations via the AugmentationStack class. At a high level, the user creates the object by passing in a list of transformations and specifying the order in which they should be applied to the data batch, the loss tensor, or both. Separate AugmentationStacks are applied automatically by the LFADS object during training and inference, making it much easier to experiment with new augmentation strategies.

The LFADS model computes KL penalties between posteriors and priors for both initial condition and inferred input distributions, which are added to the reconstruction cost in the variational ELBO. In the original implementation, priors were multivariate normal and autoregressive multivariate normal for the initial condition and inferred inputs, respectively. In lfads-torch, the user can easily switch between priors using the ic_prior and co_prior config arguments or create custom prior modules by implementing make_posterior and forward functions. This gives users the freedom to experiment with alternative priors that may be more appropriate for certain brain areas and tasks.

1. David Sussillo, Rafal Jozefowicz, LF Abbott, and Chethan Pandarinath. LFADS – Latent Factor Analysis via Dynamical Systems. arXiv preprint arXiv:1608.06315, 2016.
2. Chethan Pandarinath, Daniel J O’Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D Stavisky, Jonathan C Kao, Eric M Trautmann, Matthew T Kaufman, Stephen I Ryu, Leigh R Hochberg, et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 15(10):805–815, 2018.
3. Mohammad Reza Keshtkaran and Chethan Pandarinath. Enabling hyperparameter optimization in sequential autoencoders for spiking neural data. Advances in Neural Information Processing Systems, 32, 2019.
4. Mohammad Reza Keshtkaran*, Andrew R Sedler*, Raeed H Chowdhury, Raghav Tandon, Diya Basrai, Sarah L Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E Miller, and Chethan Pandarinath. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nature Methods, 19(12), 2022.
5. Lahiru N Wimalasena, Jonas F Braun, Mohammad Reza Keshtkaran, David Hofmann, Juan ́Alvaro Gallego, Cristiano Alessandro, Matthew C Tresch, Lee E Miller, and Chethan Pandarinath. Estimating muscle activation from emg using deep learning-based dynamical systems models. Journal of Neural Engineering, 19(3):036013, 2022.
6. Feng Zhu*, Andrew R Sedler*, Harrison A Grier, Nauman Ahad, Mark Davenport, Matthew Kaufman, Andrea Giovannucci, and Chethan Pandarinath. Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. Advances in Neural Information Processing Systems, 34:2331–2345, 2021.
7. Feng Zhu, Harrison A Grier, Raghav Tandon, Changjia Cai, Anjali Agarwal, Andrea Giovannucci, Matthew T Kaufman, and Chethan Pandarinath. A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution. Nature Neuroscience, 19(12), 2022.
8. Brianna M Karpowicz, Yahia H Ali, Lahiru N Wimalasena, Andrew R Sedler, Mohammad Reza Keshtkaran, Kevin Bodkin, Xuan Ma, Lee E Miller, and Chethan Pandarinath. Stabilizing brain-computer interfaces through alignment of latent dynamics. bioRxiv, 2022.
9. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 2019.
10. William Falcon and The PyTorch Lightning team. PyTorch Lightning, 3 2019. URL https://github.com/Lightning-AI/lightning.
11. Omry Yadan. Hydra - A framework for elegantly configuring complex applications. Github, 2019. URL https://github.com/facebookresearch/hydra.
12. Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118, 2018.