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Related Experiment Video

Updated: Jan 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.

Nishal P Shah¹, Benyamin Abramovich Krasa², Erin Kunz²

¹Rice University.

Biorxiv : the Preprint Server for Biology

|November 19, 2025

Summary

This summary is machine-generated.

We introduce contextual LFADS, a modified Latent Factor Analysis using Dynamical Systems (LFADS) model. This enhances interpretability of neural dynamics by enabling context-specific adaptation, improving brain-computer interface (BCI) applications.

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Area of Science:

Computational neuroscience
Dynamical systems theory
Machine learning for neuroscience

Background:

Biological neural circuits process information through evolving internal states.
Latent Factor Analysis using Dynamical Systems (LFADS) models neural activity with RNNs but suffers from poor interpretability due to complex dynamics.

Purpose of the Study:

To improve the interpretability of LFADS models for neural dynamics.
To develop a modified LFADS model that captures trial-specific contextual information.
To address challenges in brain-computer interface (BCI) data analysis.

Main Methods:

Introduced a per-trial bias to the LFADS recurrent neural network (RNN) generator.
Modeled the per-trial bias as a constant input for contextual adaptation.
Tested the modified model (contextual LFADS) on simulated and real neural recordings.

Main Results:

Standard LFADS exhibited complex, multi-stable dynamics; contextual LFADS learned simpler, interpretable dynamics.
Contextual LFADS enabled single-trial analysis, reproducing trial-averaged results.
The modified model addressed non-stationarity and behavioral variability in BCI recordings.

Conclusions:

Contextual LFADS provides a more interpretable model of neural dynamics.
The per-trial bias modification enhances the utility of LFADS for analyzing neural data and BCI.
This approach offers a promising method for understanding and utilizing neural signals.