NEURAL NETWORKS THAT LEARN TEMPORAL SEQUENCES BY SELECTION
NEURAL NETWORKS THAT LEARN TEMPORAL SEQUENCES BY SELECTION
复制标题
DOI：
10.1073/pnas.84.9.2727
复制
发表时间：
1987-05-01
影响因子：
11.1
通讯作者：
NADAL, JP
中科院分区：
文献类型：
--
作者：
DEHAENE, S;CHANGEUX, JP;NADAL, JP
正式的神经网络模型，学习时间序列的选择提出的基础上，对鸟类的歌曲收购的观察，序列检测神经元，和变构受体。该模型依赖于由三个神经元组成的假设的基本设备，即突触三联体，其通过异突触相互作用产生突触功效的短期修改，以及局部赫布学习规则。假设的功能单位是相互抑制的协同神经元簇和突触束。在此基础上形成的网络显示被动识别和生产的时间序列，可能包括重复的能力。学习规则的引入导致序列检测神经元的分化和正在进行的时间序列的稳定。由三层神经元簇组成的网络架构表现出主动识别和学习的时间序列的选择：网络自发地产生prerepresentations的选择，根据他们的共振与输入感知。模型的预测进行了讨论。
A model for formal neural networks that learn temporal sequences by selection is proposed on the basis of observations on the acquisition of song by birds, on sequence-detecting neurons, and on allosteric receptors. The model relies on hypothetical elementary devices made up of three neurons, the synaptic triads, which yield short-term modification of synaptic efficacy through heterosynaptic interactions, and on a local Hebbian learning rule. The functional units postulated are mutually inhibiting clusters of synergic neurons and bundles of synapses. Networks formalized on this basis display capacities for passive recognition and for production of temporal sequences that may include repetitions. Introduction of the learning rule leads to the differentiation of sequence detecting neurons and to the stabilization of ongoing temporal sequences. A network architecture composed of three layers of neuronal clusters is shown to exhibit active recognition and learning of time sequences by selection: the network spontaneously produces prerepresentations that are selected according to their resonance with the input percepts. Predictions of the model are discussed.