Computational properties of networks of synchronous groups of spiking neurons
Computational properties of networks of synchronous groups of spiking neurons
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DOI：
10.1162/neco.2007.19.9.2433
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发表时间：
2007-09-01
影响因子：
2.9
通讯作者：
Dayhoff, Judith E.
中科院分区：
文献类型：
--
作者：
Dayhoff, Judith E.
我们展示了一个模型，在这个模型中，同步发射的神经元集合被联网来产生计算结果。每一个集合都是一组生物整合和触发神经元，组与组之间有概率的相互联系。一个类比是，人工神经网络的每个单独处理单元对应于生物模型中的一个神经元群。人工神经网络中一个单元的激活值对应于生物神经元群中同步放电的活跃神经元的比例。人工神经网络的权值对应于同步群模型中群间互连密度、突触前群的群大小和突触后电位高度的乘积。这三个参数都可以调节同步组模型中神经元组之间的连接强度。我们给出了一个非线性分类（XOR）的例子和一个函数逼近的例子，在这个例子中，人工神经网络的能力可以通过一个神经网络模型来捕获，该神经网络模型将生物整合和激活神经元配置为这些神经元的同步发射集合网络。我们指出，前馈人工神经网络证明的一般函数逼近能力似乎是由同步放电的神经元群网络近似的，其中神经元群包括整合和放电神经元。我们讨论了这种类型的生物系统模型的优势，其可能的学习机制，以及相关的时序关系。
We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.