Application of the neural data transformer to non-autonomous dynamical systems

The Neural Data Transformer (NDT) is a novel non-recurrent neural network designed to model neural population activity, offering faster inference times and the potential to advance real-time applications in neuroscience. In this study, we expand the applicability of the NDT to non-autonomous dynamical systems by investigating its performance on modeling data from the Chaotic Recurrent Neural Network (RNN) with delta pulse inputs. Through adjustments to the NDT architecture, we demonstrate its capability to accurately capture non-autonomous neural population dynamics, making it suitable for a broader range of Brain-Computer Inter-face (BCI) control applications. Additionally, we introduce a modification to the model that enables the extraction of interpretable inferred inputs, further enhancing the utility of the NDT as a powerful and versatile tool for real-time BCI applications.
2023 Summer.
Includes bibliographical references.
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