Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

TL;DR

This paper introduces recognizing RNNs (rRNNs), which integrate Bayesian inference with recurrent neural networks to improve biological plausibility and computational power, enabling fast, robust decoding of dynamic stimuli.

Contribution

It proposes a novel Bayesian inference framework for RNNs, creating recognizing RNNs that perform predictive coding and dynamic stimulus decoding.

Findings

rRNNs enable fast decoding of dynamic inputs

They are robust to noise and initial conditions

Demonstrated in decoding human kinematics

Abstract

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics.