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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Associative Memories With Synaptic Delays
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Associative Memories With Synaptic Delays

机译：具有突触延迟的联想记忆

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摘要

In this paper, we introduce a new concept of associative memories in which synaptic connections of the self-organizing neural network learn time delays between input sequence elements. Synaptic connections represent both the synaptic weights and expected delays between the network inputs. This property of synaptic connections facilitates recognition of time sequences and provides context-based associations between sequence elements. Characteristics of time delays are learned and are updated each time an input sequence is presented. There are no separate learning and testing modes typically used in other neural networks, as the network starts to predict the next input element as soon as there is no expected input signal. The network generates output signals useful for associative recall and prediction. These output signals depend on the presented input context and the knowledge stored in the graph. Such a mode of operation is preferred for the organization of episodic memories used to store the observed episodes and to recall them if a sufficient context is provided. The associative sequential recall is useful for the operation of working memory in a cognitive agent. Test results demonstrate that the network correctly recognizes the input sequences with variable delays and that it is more efficient than other recently developed sequential memory networks based on associative neurons.
机译：在本文中，我们介绍了一种联想记忆的新概念，其中自组织神经网络的突触连接学习输入序列元素之间的时间延迟。突触连接既代表突触权重，又代表网络输入之间的预期延迟。突触连接的这种特性有助于时间序列的识别，并在序列元素之间提供基于上下文的关联。每次输入一个输入序列时，都会学习并更新时间延迟特性。在其他神经网络中通常没有单独的学习和测试模式，因为一旦没有预期的输入信号，网络就会开始预测下一个输入元素。网络生成可用于关联召回和预测的输出信号。这些输出信号取决于所呈现的输入上下文和图中存储的知识。对于组织用于存储观察到的情节并在提供足够上下文的情况下调用它们的情节性记忆的组织，这种操作模式是优选的。关联顺序回忆对于认知代理中的工作记忆操作非常有用。测试结果表明，该网络可以正确地识别具有可变延迟的输入序列，并且比基于关联神经元的其他最近开发的顺序存储网络更有效。

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