# Enabling hyperparameter optimization in sequential autoencoders for spiking neural data

arXiv: https://arxiv.org/abs/1908.07896 · HTML: https://mlsanity.com/paper/1908.07896/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024)
- [A survey on multi-objective hyperparameter optimization algorithms for Machine Learning](https://mlsanity.com/paper/2111.13755/) (2021)
- [Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics](https://mlsanity.com/paper/2206.02972/) (2022)
- [Latent Diffusion for Neural Spiking Data](https://mlsanity.com/paper/2407.08751/) (2024)
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- [lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems](https://mlsanity.com/paper/2309.01230/) (2023)
- [Brain-to-Text Benchmark '24: Lessons Learned](https://mlsanity.com/paper/2412.17227/) (2024)
- [SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding](https://mlsanity.com/paper/2507.08402/) (2025)
- [Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time](https://mlsanity.com/paper/2111.00070/) (2021)
- [Inferring Population Dynamics in Macaque Cortex](https://mlsanity.com/paper/2304.06040/) (2023)
- [Predictive variational autoencoder for learning robust representations of time-series data](https://mlsanity.com/paper/2312.06932/) (2023)
- [Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes](https://mlsanity.com/paper/2407.21195/) (2024)
- [Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment](https://mlsanity.com/paper/2601.19963/) (2026)
- [Langevin Flows for Modeling Neural Latent Dynamics](https://mlsanity.com/paper/2507.11531/) (2025)
- [Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior](https://mlsanity.com/paper/2603.05612/) (2026)
- [Stable Neural Decoding Across Sessions via Task-Conditioned Latent Alignment for Brain-Machine Interfaces](https://mlsanity.com/paper/2609.27441/) (2026)

## Abstract (arXiv)

Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from high-dimensional datasets. One recent line of work uses recurrent neural networks in a sequential autoencoder (SAE) framework to uncover dynamics. SAEs are an appealing option for modeling nonlinear dynamical systems, and enable a precise link between neural activity and behavior on a single-trial basis. However, the very large parameter count and complexity of SAEs relative to other models has caused concern that SAEs may only perform well on very large training sets. We hypothesized that with a method to systematically optimize hyperparameters (HPs), SAEs might perform well even in cases of limited training data. Such a breakthrough would greatly extend their applicability. However, we find that SAEs applied to spiking neural data are prone to a particular form of overfitting that cannot be detected using standard validation metrics, which prevents standard HP searches. We develop and test two potential solutions: an alternate validation method ("sample validation") and a novel regularization method ("coordinated dropout"). These innovations prevent overfitting quite effectively, and allow us to test whether SAEs can achieve good performance on limited data through large-scale HP optimization. When applied to data from motor cortex recorded while monkeys made reaches in various directions, large-scale HP optimization allowed SAEs to better maintain performance for small dataset sizes. Our results should greatly extend the applicability of SAEs in extracting latent dynamics from sparse, multidimensional data, such as neural population spiking activity.