瞬态动力学神经网络
Neural networks with transient state dynamics
摘要：
我们调查动力系统的特点是一个时间序列的不同的半稳定的活动模式，因为他们观察到的皮层神经活动模式。我们提出并讨论了一个一般的机制，允许吸引子网络和一个特定的邻接瞬态网络，这是严格耗散之间的绝热连续。具有瞬态的动态系统在其工作点被自动调节时保持功能性;避免长时间的停滞或漂移到快速波动的状态。我们表明，在一个连续时间的神经网络模型，一个单一的本地更新规则的在线学习允许同时（i）通过无监督的赫布型学习的信息存储，（ii）自适应调节的工作点和（iii）抑制失控的突触生长。给出了模拟结果，讨论了时间反演对称性和链路对称性的自发破缺。
英文摘要：
We investigate dynamical systems characterized by a time series of distinct semi-stable activity patterns, as they are observed in cortical neural activity patterns. We propose and discuss a general mechanism allowing for an adiabatic continuation between attractor networks and a specific adjoined transient-state network, which is strictly dissipative. Dynamical systems with transient states retain functionality when their working point is autoregulated; avoiding prolonged periods of stasis or drifting into a regime of rapid fluctuations. We show, within a continuous-time neural network model, that a single local updating rule for online learning allows simultaneously (i) for information storage via unsupervised Hebbian-type learning, (ii) for adaptive regulation of the working point and (iii) for the suppression of runaway synaptic growth. Simulation results are presented; the spontaneous breaking of time-reversal symmetry and link symmetry are discussed.
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