Optimal information storage and the distribution of synaptic weights: Perceptron versus Purkinje cell
Optimal information storage and the distribution of synaptic weights: Perceptron versus Purkinje cell
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DOI：
10.1016/s0896-6273(04)00528-8
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发表时间：
2004-09-02
期刊：
影响因子：
16.2
通讯作者：
Barbour, B
中科院分区：
文献类型：
--
作者：
Brunel, N;Hakim, V;Barbour, B
人们普遍认为，突触修饰是学习和记忆的基础。然而，很少有研究研究从突触权重的分布中可以推断出学习过程。我们对典型的前馈神经网络感知器进行了分析，得到了具有兴奋性突触的感知器的最优突触权重分布。它包含50%以上的无声突触，这一比例随着存储可靠性的增加而增加：因此，无声突触是优化学习和可靠性的必要副产品。利用感知器和小脑浦肯野细胞之间的经典类比，我们将最优重量分布与颗粒细胞-浦肯野细胞突触的测量结果进行了拟合。这两个分布吻合得很好，这表明浦肯野细胞可以学习多达5千字节的信息，形式为40,000个输入-输出关联。
It is widely believed that synaptic modifications underlie learning and memory. However, few studies have examined what can be deduced about the learning process from the distribution of synaptic weights. We analyze the perceptron, a prototypical feedforward neural network, and obtain the optimal synaptic weight distribution for a perceptron with excitatory synapses. It contains more than 50% silent synapses, and this fraction increases with storage reliability: silent synapses are therefore a necessary byproduct of optimizing learning and reliability. Exploiting the classical analogy between the perceptron and the cerebellar Purkinje cell, we fitted the optimal weight distribution to that measured for granule cell-Purkinje cell synapses. The two distributions agreed well, suggesting that the Purkinje cell can learn up to 5 kilobytes of information, in the form of 40,000 input-output associations.