Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo Kashiwa, Japan ; Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute Wako, Japan.
Front Comput Neurosci. 2013 Feb 7;6:102. doi: 10.3389/fncom.2012.00102. eCollection 2012.
The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.
在海马体和新皮层中，锥体神经元的突触后电位具有非高斯幅度分布，尾部较重。最近的研究表明，这种突触权重分布会产生自发的内部噪声，有利于皮层回路中的尖峰传播。然而，这种由长尾权重分布产生的内部噪声对于其他计算功能是否可行且有益仍然未知。为了阐明这一点，我们构建了一个具有尖峰神经元的联想记忆（AM）网络模型，该模型在具有对数正态权重分布的连接矩阵中存储多个记忆模式。在 AM 网络中，未检索到的记忆模式会产生严重干扰记忆回忆的串扰噪声。我们证明，在我们的模型中，编码检索到的记忆模式的神经元和编码未检索到的记忆模式的神经元具有不同的亚阈值膜电位分布。因此，对于编码神经元，对强突触输入做出响应的概率增加，而对于非编码神经元，对强突触输入做出响应的概率降低。我们的结果表明，连接权重的长尾分布可以产生有用的 AM 回忆噪声。
