Modelling memory functions with recurrent neural networks consisting of input compensation units:: I.: Static situations
Modelling memory functions with recurrent neural networks consisting of input compensation units:: I.: Static situations
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DOI：
10.1007/s00422-006-0137-x
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发表时间：
2007-05-01
影响因子：
1.9
通讯作者：
Cruse, Holk
中科院分区：
文献类型：
--
作者：
Kuehn, Simone;Beyn, Wolf-Juergen;Cruse, Holk
人类能够对他们所处理的信息形成内部表征——这种能力使他们能够执行许多不同的记忆任务。因此，神经系统必须以某种方式学习表征环境状况的各个方面；这个过程被认为是基于突触的变化。要表示的情况是各种各样的，例如不同类型的静态模式，也有动态场景。由相互连接的神经元组成的神经网络如何能够完成这样的任务？本文提出了一种新的人工神经元结构。这种结构允许人们从学习过程引起的突触变化引起的动态中分离出周期性连接的动态。误差信号在单个神经元内局部计算。因此，无需任何额外结构就可以实现在线学习。配备这些计算单元的循环神经网络可以处理不同的记忆任务。举例说明如何从包含固定模式的环境情况中提取信息，以产生持续的活动并处理简单的代数关系。
Humans are able to form internal representations of the information they process-a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.