Learning with two sites of synaptic integration
Learning with two sites of synaptic integration
复制标题
利用两个突触整合位点进行学习
DOI：
10.1088/0954-898x_11_1_302
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
P. König
中科院分区：
文献类型：
--
作者：
Konrad Paul Kording;P. König
自D O Hebb 1949年的经典著作The Organization of Behaviour（纽约：Wiley）以来，人们认为突触可塑性仅取决于突触前和突触后细胞的活动。突触专门通过突触后活动影响其他突触的可塑性。这混淆了对突触可塑性和神经元激活的影响，因此，难以实现优化全局性能测量的网络。探索这个问题的解决方案，启发最近的研究顶端树突的属性，我们研究了一个网络的神经元与两个网站的突触整合。这些突触以这样一种方式进行交流，一组突触主要影响神经元的活动，另一组则控制突触的可塑性。用一组恒定的参数来分析这个系统，可以发现：（1）控制可塑性的传入神经对每个细胞都起着监督者的作用。(2)当神经元获得特定的感受野时，对于不同的刺激，净活动保持恒定。这确保了所有刺激都被表示，从而有助于信息最大化。(3)最大化一致信息的机制可以很容易地实现。具有非重叠感受野的神经元学习发射相关的并且优先传输在空间上相关的信息。(4)我们演示了如何可以实现一个新的性能衡量标准：细胞学习，以表示只有一部分的输入是相关的处理在更高的阶段。这一标准被称为“相关信息”。
Since the classical work of D O Hebb 1949 The Organization of Behaviour (New York: Wiley) it is assumed that synaptic plasticity solely depends on the activity of the pre- and the post-synaptic cells. Synapses influence the plasticity of other synapses exclusively via the post-synaptic activity. This confounds effects on synaptic plasticity and neuronal activation and, thus, makes it difficult to implement networks which optimize global measures of performance. Exploring solutions to this problem, inspired by recent research on the properties of apical dendrites, we examine a network of neurons with two sites of synaptic integration. These communicate in such a way that one set of synapses mainly influences the neurons' activity; the other set gates synaptic plasticity. Analysing the system with a constant set of parameters reveals: (1) the afferents that gate plasticity act as supervisors, individual to every cell. (2) While the neurons acquire specific receptive fields the net activity remains constant for different stimuli. This ensures that all stimuli are represented and, thus, contributes to information maximization. (3) Mechanisms for maximization of coherent information can easily be implemented. Neurons with non-overlapping receptive fields learn to fire correlated and preferentially transmit information that is correlated over space. (4) We demonstrate how a new measure of performance can be implemented: cells learn to represent only the part of the input that is relevant to the processing at higher stages. This criterion is termed ‘relevant infomax’.