REDUCTION OF CONDUCTANCE-BASED MODELS WITH SLOW SYNAPSES TO NEURAL NETS
REDUCTION OF CONDUCTANCE-BASED MODELS WITH SLOW SYNAPSES TO NEURAL NETS
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DOI：
10.1162/neco.1994.6.4.679
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发表时间：
1994-07-01
影响因子：
2.9
通讯作者：
ERMENTROUT, B
中科院分区：
文献类型：
--
作者：
ERMENTROUT, B
本文采用平均化方法和详细的分岔计算，将一个突触耦合神经元系统简化为一个Hopfield型连续时间神经网络。由于分叉的一些特殊性质，不需要显式平均，约化成为一个简单的代数问题。由此产生的计算显示了如何推导出一种新型的“挤压函数”，其性质与膜的详细离子机制直接相关。频率编码与幅度编码相对，从理论上看是一种自然的方式。对完整系统和简化系统进行了数值比较。
The method of averaging and a detailed bifurcation calculation are used to reduce a system of synaptically coupled neurons to a Hopfield type continuous time neural network. Due to some special properties of the bifurcation, explicit averaging is not required and the reduction becomes a simple algebraic problem. The resultant calculations show one how to derive a new type of ''squashing function'' whose properties are directly related to the detailed ionic mechanisms of the membrane. Frequency encoding as opposed to amplitude encoding emerges in a natural fashion from the theory. The full system and the reduced system are numerically compared.