Group for Neural Theory, Laboratoire des Neurosciences Cognitives et Computationelles INSERM U960, Ecole Normale Superieure PSL* University, Paris, France.
UMR 8520 CNRS, IEMN, Villeneuve d'Asqu, 59650, France.
Sci Rep. 2024 Aug 6;14(1):18226. doi: 10.1038/s41598-024-65866-9.
Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), which does not require inhibition contrary to the exclusive-or function (XOR). Notably, sublinear dendritic operations enable LNSCs when scattered synaptic activation generates increased somatic spike output. However, experimental demonstrations of scatter-sensitive neuronal computations have not yet been described. Using glutamate uncaging onto cerebellar molecular layer interneurons, we show that scattered synaptic-like activation of dendrites evoked larger compound EPSPs than clustered synaptic activation, generating a higher output spiking probability. Moreover, we also demonstrate that single interneurons can indeed implement the FBP. Using a biophysical model to explore the conditions in which a neuron might be expected to implement the FBP, we establish that sublinear summation is necessary but not sufficient. Other parameters such as the relative sublinearity, the EPSP size, depolarization amplitude relative to action potential threshold, and voltage fluctuations all influence whether the FBP can be performed. Since sublinear synaptic summation is a property of passive dendrites, we expect that many different neuron types can implement LNSCs.
理论预测，树突内突触电位的非线性总和使神经元能够执行线性不可分计算（LNSC）。使用布尔分析方法，我们预测超线性和亚线性突触总和都可以使单个神经元实现一种 LNSC，即特征绑定问题（FBP），它不需要抑制，与异或功能（XOR）相反。值得注意的是，当分散的突触激活产生增加的体刺输出时，亚线性树突操作允许 LNSC。然而，尚未描述对分散敏感的神经元计算的实验证明。使用谷氨酸光解作用于小脑分子层中间神经元，我们表明，与簇状突触激活相比，树突的分散突触样激活引发更大的复合 EPSP，从而产生更高的输出刺发放电概率。此外，我们还证明单个中间神经元确实可以实现 FBP。使用生物物理模型来探索神经元可能实现 FBP 的条件，我们确定亚线性总和是必要的，但不是充分的。其他参数，如相对亚线性、EPSP 大小、相对于动作电位阈值的去极化幅度以及电压波动，都会影响是否可以执行 FBP。由于亚线性突触总和是被动树突的一个特性，我们预计许多不同的神经元类型都可以实现 LNSC。
