Amarasingham A, Levy W B
Echols Scholars Program, College of Arts and Sciences, University of Virginia, Charlottesville 22908, USA.
Neural Comput. 1998 Jan 1;10(1):25-57. doi: 10.1162/089976698300017881.
This article investigates the synaptic weight distribution of a self-supervised, sparse, and randomly connected recurrent network inspired by hippocampal region CA3. This network solves nontrivial sequence prediction problems by creating, on a neuron-by-neuron basis, special patterns of cell firing called local context units. These specialized patterns of cell firing--possibly an analog of hippocampal place cells--allow accurate prediction of the statistical distribution of synaptic weights, and this distribution is not at all gaussian. Aside from the majority of synapses that are, at least functionally, lost due to synaptic depression, the distribution is approximately uniform. Unexpectedly, this result is relatively independent of the input environment, and the uniform distribution of synaptic weights can be approximately parameterized based solely on the average activity level. Next, the results are generalized to other cell firing types (frequency codes and stochastic firing) and place cell-like firing distributions. Finally, we note that our predictions concerning the synaptic strength distribution can be extended to the distribution of correlated cell firings. Recent published neurophysiological results are consistent with this extension.
本文研究了受海马体CA3区启发的自监督、稀疏且随机连接的递归网络的突触权重分布。该网络通过逐个神经元地创建称为局部上下文单元的特殊细胞放电模式，解决了非平凡的序列预测问题。这些特殊的细胞放电模式——可能类似于海马体位置细胞——允许准确预测突触权重的统计分布，并且这种分布完全不是高斯分布。除了由于突触抑制而至少在功能上丢失的大多数突触外，该分布近似均匀。出乎意料的是，这一结果相对独立于输入环境，并且突触权重的均匀分布可以仅基于平均活动水平进行近似参数化。接下来，将结果推广到其他细胞放电类型（频率编码和随机放电）以及类似位置细胞的放电分布。最后，我们注意到我们关于突触强度分布的预测可以扩展到相关细胞放电的分布。最近发表的神经生理学结果与这一扩展一致。
