SEQUENTIAL STATE GENERATION BY MODEL NEURAL NETWORKS
SEQUENTIAL STATE GENERATION BY MODEL NEURAL NETWORKS
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DOI：
10.1073/pnas.83.24.9469
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发表时间：
1986-12-01
影响因子：
11.1
通讯作者：
KLEINFELD, D
中科院分区：
文献类型：
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作者：
KLEINFELD, D
神经输出活动的顺序模式构成了许多生物过程的基础，例如控制运动的循环输出模式。我展示了如何通过一类模型神经网络生成这样的序列，这些模型神经网络在选定的记忆状态之间进行定义的转换集。序列生成网络依赖于两组突触连接之间的相互作用。其中一组的作用是将网络稳定在当前的记忆状态，而另一组的作用是延迟的，它使网络在记忆之间进行指定的转换。这些网络的动态特性是根据沿能量面运动来描述的。通过数值算例说明了网络在完整连接和有噪声或缺失连接情况下的性能。此外，本文还提出了一种利用这些网络识别外部生成序列的方案。
Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks.