自主活动循环神经网络的语义学习
Semantic learning in autonomously active recurrent neural networks
摘要：
人脑是自主活动的，其特征在于即使在没有外部感觉刺激的情况下也会存在的自我维持的神经活动。在这里，我们研究自主活动的递归神经网络和外部感官刺激的自我维持的活动之间的相互关系。外部刺激的涌入与自主和持续的大脑动力学内部产生的模式之间没有先验的语义关系。那么问题就来了，内部和外部动态过程之间的语义相关性是何时以及如何习得和建立起来的？我们研究这个问题的瞬态动力学的范式内的神经活动在循环神经网络，即一个自治的神经活动的特点是由一个无限的时间序列的瞬态稳定的吸引子状态。我们建议，外部刺激将是相关的敏感期，{ \it即}过渡期之间的一个瞬态和随后的半稳定吸引子。每当刺激定性地影响内部动态时，无监督地生成扩散学习信号。为了测试，我们已经提出了相应的酒吧和条纹问题的模型系统的刺激。我们发现，该系统执行非线性独立分量分析本身，是连续和自主的活动。这种涌现的认知能力是由神经动力学的一个普遍原理--神经集合之间的竞争--产生的。
英文摘要：
The human brain is autonomously active, being characterized by a self-sustained neural activity which would be present even in the absence of external sensory stimuli. Here we study the interrelation between the self-sustained activity in autonomously active recurrent neural nets and external sensory stimuli. There is no a priori semantical relation between the influx of external stimuli and the patterns generated internally by the autonomous and ongoing brain dynamics. The question then arises when and how are semantic correlations between internal and external dynamical processes learned and built up? We study this problem within the paradigm of transient state dynamics for the neural activity in recurrent neural nets, i.e. for an autonomous neural activity characterized by an infinite time-series of transiently stable attractor states. We propose that external stimuli will be relevant during the sensitive periods, {\it viz} the transition period between one transient state and the subsequent semi-stable attractor. A diffusive learning signal is generated unsupervised whenever the stimulus influences the internal dynamics qualitatively. For testing we have presented to the model system stimuli corresponding to the bars and stripes problem. We found that the system performs a non-linear independent component analysis on its own, being continuously and autonomously active. This emergent cognitive capability results here from a general principle for the neural dynamics, the competition between neural ensembles.
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