Cortico-cerebellar networks as decoupling neural interfaces
Cortico-cerebellar networks as decoupling neural interfaces
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发表时间：
2021-10
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通讯作者：
J. Pemberton;E. Boven;Richard Apps;R. P. Costa
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作者：
J. Pemberton;E. Boven;Richard Apps;R. P. Costa
大脑很好地解决了信用分配问题。为了在神经网络之间分配学分，原则上，它们必须等待特定的神经计算完成。大脑如何处理这个固有的锁定问题仍然不清楚。深度学习方法在前向和反馈阶段都受到类似的锁定约束。近年来，解耦神经接口（DNIs）作为一种解决深度网络前向和反馈锁定问题的方法被引入。在这里，我们提出一个特殊的大脑区域，小脑，帮助大脑皮层解决类似于DNIs的锁定问题。为了证明这一框架的潜力，我们引入了一个系统级模型，其中一个循环皮层网络接收来自小脑模块的在线时间反馈预测。我们在许多感觉运动（线条和数字绘制）和认知任务（模式识别和标题生成）上测试了这种皮质-小脑循环神经网络（ccRNN）模型，这些任务已被证明是小脑依赖的。在所有任务中，我们观察到ccrnn在促进学习的同时减少了类似共济失调的行为，这与经典的实验观察结果一致。此外，我们的模型还解释了最近的行为和神经元观察，同时在多个层面上做出了几个可测试的预测。总的来说，我们的工作为小脑作为高效信用分配的全脑解耦机提供了一个新的视角，并在深度学习和神经科学之间开辟了一条新的途径。
The brain solves the credit assignment problem remarkably well. For credit to be assigned across neural networks they must, in principle, wait for specific neural computations to finish. How the brain deals with this inherent locking problem has remained unclear. Deep learning methods suffer from similar locking constraints both on the forward and feedback phase. Recently, decoupled neural interfaces (DNIs) were introduced as a solution to the forward and feedback locking problems in deep networks. Here we propose that a specialised brain region, the cerebellum, helps the cerebral cortex solve similar locking problems akin to DNIs. To demonstrate the potential of this framework we introduce a systems-level model in which a recurrent cortical network receives online temporal feedback predictions from a cerebellar module. We test this cortico-cerebellar recurrent neural network (ccRNN) model on a number of sensorimotor (line and digit drawing) and cognitive tasks (pattern recognition and caption generation) that have been shown to be cerebellar-dependent. In all tasks, we observe that ccRNNs facilitates learning while reducing ataxia-like behaviours, consistent with classical experimental observations. Moreover, our model also explains recent behavioural and neuronal observations while making several testable predictions across multiple levels. 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 between deep learning and neuroscience.