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

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

2012-01-20

arxiv.org/abs/1201.4339v1

Biology

Quantitative Biology

Neurons and Cognition

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-321209