CORTICO-CEREBELLAR NETWORKS AS DECOUPLED NEURAL INTERFACES

Abstract

The brain solves the credit assignment problem remarkably well. For credit to be correctly assigned across multiple cortical areas a given area should, in principle, wait for others to finish their computation. How the brain deals with this locking problem has remained unclear. Deep learning methods suffer from similar locking constraints both on the forward and backward phase. Recently, decoupled neural interfaces (DNI) were introduced as a solution to the forward and backward locking problems. Here we propose that a specialised brain region, the cerebellum, helps the cerebral cortex solve the locking problem closely matching the computations and architecture of DNI. In particular, we propose that classical cerebellar forward and inverse models are equivalent to solving the backward and forward locking problems, respectively. To demonstrate the potential of this framework we focus on modelling a given brain area as a recurrent neural network in which the cerebellum approximates temporal feedback signals as provided by BPTT. We tested the cortico-cerebellar-DNI (CC-DNI) model in a range of sensorimotor and cognitive tasks that have been shown to be cerebellar-dependent. First, we show that the CC-DNI unlocking mechanisms can facilitate learning in a simple target reaching task. Next, by building on the sequential MNIST task we demonstrate that these results generalise to more complex sensorimotor tasks. Our cortico-cerebellar model readily applies to a wider range of modalities, to demonstrate this we tested the model in a cognitive task, caption generation. Models without the cerebellar-DNI component exhibit deficits similar to those observed in cerebellar patients in both motor and cognitive tasks. Moreover, we used CC-DNI to generate a set of specific neuroscience predictions. Finally, we introduce a CC-DNI model with highly sparse connectivity as observed in the cerebellum, which substantially reduces the number of parameters while improving learning through decorrelation. Overall, our work offers a novel perspective on the cerebellum as a brain-wide decoupling machine for efficient credit assignment and opens a new avenue of research between deep learning and neuroscience.

1. INTRODUCTION

Efficient credit assignment in the brain is a critical part of learning. However, how the brain solves the credit assignment problem remains a mystery. One of the central issues of credit assignment across multiple stages of processing is the need to wait for previous stages to finish their computation before others can proceed (Rumelhart et al., 1986; Schmidhuber, 1990; Lee et al., 2015; Marblestone et al., 2016; Jaderberg et al., 2017) . In deep artificial neural networks these constraints are explicit. During the forward phase a given layer has to wait for all its previous layers to finish before it can proceed, a constraint known as the forward lock. Similarly, during the backward phase a given layer has to wait for all the layers above to finish computing its gradients -backward lock. Recently, a framework was introduced to decouple artificial neural networks -decoupled neural interfaces (DNI; (Jaderberg et al., 2017 ))foot_0 , effectively breaking forward and/or backward locks. Here, we propose that a specialised brain area, the cerebellum, performs a similar role in the brain. In the classical view the cerebellum is key for fine motor control and learning by constructing internal models of behaviour. (Marr, 1969; Albus, 1971; Raymond and Medina, 2018; Wolpert et al., 1998;  



DNIs are related to earlier work on using network critics to train neural networks(Schmidhuber, 1990).1

