论文标题
复发神经网络中的基线控制多任务
Multi-tasking via baseline control in recurrent neural networks
论文作者
论文摘要
动物行为状态（例如唤醒和运动）的变化诱导{基线输入电流的复杂调制到感觉区域，从而引发了特定于感官的方式。缺少一个简单的计算原理，解释了基线调制对复发性皮质电路的影响。我们使用储层计算方法在具有随机耦合的复发性神经网络中研究了基线调制的好处。基线调制解锁了一组新的网络阶段和现象，包括改善混乱，神经滞后和超生性破裂。令人惊讶的是，基线调制使水库网络能够执行多个任务，而无需对网络耦合进行任何优化。}网络动力学的基线控制为脑启发的人工智能打开了新的方向，并为皮质活动的行为调制提供了新的启示。
Changes in an animal's behavioral state, such as arousal and movements, induce {complex modulations of the baseline input currents to sensory areas, eliciting sensory modality-specific effects. A simple computational principle explaining the effects of baseline modulations to recurrent cortical circuits is lacking. We investigate the benefits of baseline modulations using a reservoir computing approach in recurrent neural networks with random couplings. Baseline modulations unlock a set of new network phases and phenomena, including chaos enhancement, neural hysteresis and ergodicity breaking. Strikingly, baseline modulations enable reservoir networks to perform multiple tasks, without any optimization of the network couplings.} Baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and sheds new light on behavioral modulations of cortical activity.
