Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better.

Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based function approximators.

SIMPL Algorithm Overview SIMPL iteratively optimizes neural tuning curves and latent trajectories by alternating between fitting curves to latent estimates and decoding latents from tuning curves, using behavior as an initial condition to aid convergence and interpretability. This EM-like approach integrates two well-established steps familiar to neuroscientists—fitting tuning curves and latent variable decoding—making it accessible and practical for broad adoption. Unlike neural network-based methods, SIMPL relies on simpler nonparametric models (e.g., kernel density estimators) and can scale efficiently to large neural datasets (e.g., hundreds of neurons over one hour of recording) without expensive hardware.

Validation on Synthetic Datasets SIMPL was evaluated on synthetic datasets closely simulating neuroscientific experiments, including a discrete two-alternative forced choice decision-making task and a continuous 2D grid cell spatial coding environment. Results showed that SIMPL rapidly converges to accurate latent trajectories and tuning curves closely matching ground truth while improving log-likelihood of the spike data and spatial information content of tuning curves. Behavioral initializations dramatically reduce issues of identifiability and local minima in the model-fitting process.

Application to Hippocampal Place Cell Data Applied to a real rodent hippocampal dataset (226 neurons recorded over 2 hours), SIMPL improved upon behaviorally-derived tuning curves by refining place fields to be smaller, more numerous, and more uniformly sized. This enhanced latent space better explained observed neural spikes and suggested that the hippocampus encodes spatial information at a higher resolution than traditional behavioral proxies alone reveal. These findings indicate SIMPL’s potential in reinterpreting neurophysiological data and revealing subtler aspects of spatial cognition.

Broader Implications and Future Directions The paper highlights SIMPL as a specific instance in a broader latent optimization class. While current components (e.g., kernel density estimation) might not scale optimally to very high-dimensional latent spaces, substituting with parametric models like neural networks is feasible at potential computational cost. Furthermore, SIMPL could be extended to account for complex neural phenomena such as replay events or theta sweeps that introduce asymmetric latent-behavior discrepancies; this may clarify predictive properties of place cell tuning curves.

Conclusion SIMPL offers a conceptually simple, fast, and scalable tool for improving latent variable estimation in neural data analysis. Its ability to effectively leverage behavioral measurements for initialization and iteratively refine latent variables and tuning curves marks a significant advance. It opens avenues for more accurate interpretations of neural population codes, especially in navigation and cognition research, and comes with theoretical connections to classical expectation-maximization methods ensuring robust performance.

 

George, T. M., Glaser, P., Stachenfeld, K., Barry, C., & Clopath, C. (2024). SIMPL: Scalable and hassle-free optimization of neural representations from behaviour. bioRxiv. https://doi.org/10.1101/2024.11.11.623030