Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Abstract

AbstractMechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be traineddirectlyon the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with thesame temporal and geometrical propertiesas those observed. This is calleddynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes asurrogatefor the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.