AI 中文总结
研究功能性突触簇对计算的因果必要性，通过训练具有分层树突段和稀疏电导突触的人工神经网络Dendrinet解决置换协方差分类任务，发现树突非线性和突触可塑性影响FSCs及性能，表明其可支持协方差结构计算。
AI 中文摘要
功能性突触簇（FSCs）是具有相关突触前活动的突触，它们共定位在同一神经元树突分支上。在皮质和海马锥体神经元学习后已观察到FSCs。然而，以前通过药理学阻断树突非线性来消融FSCs以确定因果必要性的努力可能存在混淆效应。因此，FSCs对计算是否具有因果必要性尚不清楚。在此，我们试图在计算机模拟中分离FSCs与这种潜在的混淆因素。我们在置换协方差分类（PCC）任务上训练Dendrinet，一种具有分层树突段和基于稀疏电导的突触的人工神经网络架构。我们发现，当树突非线性和突触结构可塑性都活跃时，具有树突的神经元可以被训练来解决任务并形成兴奋性和抑制性FSCs。关闭树突非线性会减少兴奋性FSCs，这与实验结果一致，并降低性能，同时意外地增加抑制性FSCs。此外，在保持非线性固定的同时打乱学习到的突触连接性会降低性能。这表明对学习到的连接性敏感，但打乱操作不仅仅改变FSCs。打乱抑制性突触特性比相应的兴奋性打乱更能降低性能，表明对抑制性组织更敏感。这项工作表明，树突分隔和学习到的突触组织可以支持协方差结构的计算。
英文摘要
Functional synapse clusters (FSCs) are synapses with correlated presynaptic activity that are colocalized on the same neuronal dendritic branch. FSCs have been observed after learning in cortical and hippocampal pyramidal neurons. However, previous efforts to ablate FSCs by pharmacologically blocking dendritic nonlinearities to establish causal necessity may have confounded effects. Therefore, whether FSCs are causally necessary for computation is unknown. Here, we attempt to isolate FSCs from this potential confounder in silico. We train Dendrinet, an artificial neural network architecture with hierarchical dendritic segments and sparse conductance-based synapses, on a Permuted-Covariance Classification (PCC) task. This task cannot be solved by single-layer linear-nonlinear artificial neural networks. We find that neurons with dendrites can be trained to solve the task and develop excitatory and inhibitory FSCs if both dendritic nonlinearities and synaptic structural plasticity are active. Turning off dendritic nonlinearities reduces excitatory FSCs, which replicates experimental findings, and reduces performance while unexpectedly increasing inhibitory FSCs. Furthermore, shuffling learned synaptic connectivity while keeping the nonlinearities fixed reduces performance. This shows sensitivity to learned connectivity, but the shuffle does not change only FSCs. Shuffling inhibitory synapse properties reduces performance more than the corresponding excitatory shuffle, showing higher sensitivity to inhibitory organization. This work suggests that dendritic compartmentalization and learned synaptic organization can support computation of covariance structure.
Comments25 pages, 7 figures
