Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks
详细信息 查看全文
- 作者：Philippe Vincent-Lamarre ; Guillaume Lajoie…
- 关键词：Reservoir computing ; Recurrent neural networks ; Chaotic networks ; Oscillations ; Perturbations ; Robustness
- 刊名：Journal of Computational Neuroscience
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：41
- 期：3
- 页码：305-322
- 全文大小：1,978 KB
- 刊物类别：Biomedical and Life Sciences
- 刊物主题：Biomedicine
Neurosciences
Neurology
Human Genetics
Theory of Computation - 出版者：Springer Netherlands
- ISSN：1573-6873
- 卷排序：41
文摘
A large body of experimental and theoretical work on neural coding suggests that the information stored in brain circuits is represented by time-varying patterns of neural activity. Reservoir computing, where the activity of a recurrently connected pool of neurons is read by one or more units that provide an output response, successfully exploits this type of neural activity. However, the question of system robustness to small structural perturbations, such as failing neurons and synapses, has been largely overlooked. This contrasts with well-studied dynamical perturbations that lead to divergent network activity in the presence of chaos, as is the case for many reservoir networks. Here, we distinguish between two types of structural network perturbations, namely local (e.g., individual synaptic or neuronal failure) and global (e.g., network-wide fluctuations). Surprisingly, we show that while global perturbations have a limited impact on the ability of reservoir models to perform various tasks, local perturbations can produce drastic effects. To address this limitation, we introduce a new architecture where the reservoir is driven by a layer of oscillators that generate stable and repeatable trajectories. This model outperforms previous implementations while being resistant to relatively large local and global perturbations. This finding has implications for the design of reservoir models that capture the capacity of brain circuits to perform cognitively and behaviorally relevant tasks while remaining robust to various forms of perturbations. Further, our work proposes a novel role for neuronal oscillations found in cortical circuits, where they may serve as a collection of inputs from which a network can robustly generate complex dynamics and implement rich computations.