IEEE Trans Cybern. 2022 Jun;52(6):4772-4783. doi: 10.1109/TCYB.2020.3042513. Epub 2022 Jun 16.
Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.
大脑中形成的神经元回路非常复杂，具有错综复杂的连接模式。在相对简单的神经元回路中，视网膜也观察到了这种复杂性。视网膜神经节细胞 (GC) 从前层的神经元接收兴奋性输入作为触发尖峰放电的驱动力。需要分析方法以系统地破译这些成分。最近，一种称为尖峰触发非负矩阵分解 (STNMF) 的方法已被提出用于此目的。在这项研究中，我们扩展了 STNMF 方法的范围。通过使用视网膜 GC 作为模型系统，我们表明 STNMF 可以检测上游双极细胞 (BC) 的各种计算特性，包括空间感受野、时间滤波器和传递非线性。此外，我们从 STNMF 的权重矩阵中恢复突触连接强度。此外，我们表明 STNMF 可以将 GC 的尖峰分为几个尖峰子集，其中每个子集由一个前馈 BC 贡献。总之，这些结果证实 STNMF 是一种用于破译神经元回路结构的有用方法。
