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首页> 外文期刊>Journal of Computational Neuroscience >A model for complex sequence learning and reproduction in neural populations
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A model for complex sequence learning and reproduction in neural populations

机译：神经种群中复杂序列学习和繁殖的模型

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Temporal patterns of activity which repeat above chance level in the brains of vertebrates and in the mammalian neocortex have been reported experimentally. This temporal structure is thought to subserve functions such as movement, speech, and generation of rhythms. Several studies aim to explain how particular sequences of activity are learned, stored, and reproduced. The learning of sequences is usually conceived as the creation of an excitation pathway within a homogeneous neuronal population, but models embodying the autonomous function of such a learning mechanism are fraught with concerns about stability, robustness, and biological plausibility. We present two related computational models capable of learning and reproducing sequences which come from external stimuli. Both models assume that there exist populations of densely interconnected excitatory neurons, and that plasticity can occur at the population level. The first model uses temporally asymmetric Hebbian plasticity to create excitation pathways between populations in response to activation from an external source. The transition of the activity from one population to the next is permitted by the interplay of excitatory and inhibitory populations, which results in oscillatory behavior that seems to agree with experimental findings in the mammalian neocortex. The second model contains two layers, each one like the network used in the first model, with unidirectional excitatory connections from the first to the second layer experiencing Hebbian plasticity. Input sequences presented in the second layer become associated with the ongoing first layer activity, so that this activity can later elicit the the presented sequence in the absence of input. We explore the dynamics of these models, and discuss their potential implications, particularly to working memory, oscillations, and rhythm generation.
机译：实验上已经报道了在脊椎动物的大脑和哺乳动物的新皮层中以高于偶然水平重复的活动的时间模式。这种时间结构被认为具有诸如运动，言语和节奏产生等功能。多项研究旨在解释如何学习，存储和复制特定的活动序列。序列的学习通常被认为是在同质神经元群体中创建一条激发途径，但是体现这种学习机制的自主功能的模型却充满了对稳定性，鲁棒性和生物学合理性的担忧。我们提出了两个相关的计算模型，它们能够学习和复制来自外部刺激的序列。两种模型都假设存在密集互连的兴奋性神经元种群，并且可塑性可在种群水平上发生。第一个模型使用时间上不对称的Hebbian可塑性来响应外部源的激活而在种群之间创建激发途径。兴奋性和抑制性种群的相互作用允许活动从一个种群过渡到下一个种群，这导致振荡行为似乎与哺乳动物新皮层的实验结果一致。第二个模型包含两层，每层都类似于第一个模型中使用的网络，从第一层到第二层的单向激励连接经历了Hebbian可塑性。第二层中显示的输入序列与正在进行的第一层活动相关联，因此该活动可以稍后在没有输入的情况下引发所显示的序列。我们探索了这些模型的动力学，并讨论了它们的潜在影响，特别是对工作记忆，振动和节奏产生的影响。

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