Prediction emerges in RNNs trained for perception

Abstract

The brain is highly proficient at making sense of noisy and ambiguous sensory inputs. Predictive processing hypothesises that this ability relies on prediction. However, it is unclear why the brain would have evolved to predict the sensory world, a computationally expensive process, in order to aid perception. Here we use simulations to argue that prediction naturally emerges in systems optimised for perception. We train recurrent neural networks (RNNs) to denoise a tokenised version of Bach's compositions at a range of noise levels. Afterwards, we enquire whether the states of the networks contain predictive information about the next token. We test this by freezing the RNN weights and training a linear readout from its states on prediction. We compare the performance of the linear readout with that of an independently trained linear benchmark model. The results show that the linear readout from the RNNs outperforms the benchmark model at moderate levels of noise, indicating that the networks rely on a predictive mechanism to support perception. We further show that the responses of the RNNs to sensory inputs are proportional to prediction error. Together, the results demonstrate that neural signatures of predictive processing emerge, without any further training constraints, from optimisation of perception.