School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom.
Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom.
Proc Natl Acad Sci U S A. 2021 Dec 7;118(49). doi: 10.1073/pnas.2102158118.
Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly's olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body's principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model's compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.
神经回路利用自身平衡补偿来实现行为的一致性，尽管其基础内在和网络参数存在可变性。然而，目前尚不清楚补偿如何调节个体中单种神经元群体的变异性，以及这种补偿可能带来什么计算上的好处。我们在果蝇的蘑菇体中解决了这些问题，这是果蝇的嗅觉记忆中心。在一个计算模型中，我们表明在稀疏编码条件下，当控制蘑菇体主要神经元——肯尼恩细胞（KCs）兴奋性的关键参数真实变化时，记忆性能会下降。然而，如果参数相互补偿以使 KC 平均活动均等化，则可以在保持真实变异性的情况下恢复记忆性能。这种补偿可以通过活动依赖和活动独立的机制来实现。最后，我们表明，我们模型的补偿机制所预测的相关性出现在果蝇半脑连接组中。这些发现揭示了蘑菇体中的补偿变异性，并描述了它对联想记忆的计算优势。
