Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data
Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data
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DOI：
10.1016/j.neuron.2018.05.038
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发表时间：
2018-07-11
期刊：
影响因子：
16.2
通讯作者：
Brunel, Nicolas
中科院分区：
文献类型：
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作者：
Pereira, Ulises;Brunel, Nicolas
吸引子神经网络场景是关联皮层记忆存储的流行场景，但基于该场景的模型与实验数据仍然存在较大差距。我们研究了一种循环网络模型，其中学习规则和存储模式的分布都是根据下颞叶皮层（ITC）中新奇和熟悉图像的视觉反应分布推断出来的。与经典吸引子神经网络模型不同，我们的模型在检索状态下表现出分级活动，其放电率分布接近对数正态。推断学习规则接近最大化无监督赫布学习规则族中存储模式的数量，这表明 ITC 中的学习规则经过优化以存储大量吸引子状态。最后，我们证明存在两种类型的检索状态：一种是发射率随时间恒定，另一种是发射率混乱波动。
The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in the inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting that learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exist two types of retrieval states: one in which firing rates are constant in time and another in which firing rates fluctuate chaotically.