Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission.
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission.
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DOI：
10.1073/pnas.0500495102
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发表时间：
2005-04
影响因子：
11.1
通讯作者：
Taro Toyoizumi;J. Pfister;K. Aihara;W. Gerstner
中科院分区：
文献类型：
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作者：
Taro Toyoizumi;J. Pfister;K. Aihara;W. Gerstner
突触神经元模型使信息传递最大化预测突触连接的变化，这种变化依赖于突触前和突触后尖峰的时间以及突触后膜电位。在泊松放电统计量的假设下，突触更新规则表现出Bienenstock-Cooper-Munro规则的所有特征，特别是突触增强和突触抑制的区域被一个滑动阈值分开。此外，该学习规则也适用于更真实的神经元模型不稳定的情况，并且即使在没有突触前频率调制的情况下，也对输入尖峰之间的相关性敏感。学习规则是在突触后放电频率保持在接近某一目标放电速率的约束下，通过最大化突触前和突触后棘波序列之间的互信息来寻找学习规则。从稳态突触过程和棘波时序依赖可塑性的角度对突触更新规则进行了讨论。
Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spike-timing-dependent plasticity is discussed.