Self-centred vs. other-centred homeostatic plasticity in inhibitory interneurons

生命的一个决定性特征是动态平衡：通过补偿偏离所需设定点的扰动，在不断变化的条件下保持不变的状态。然而，这种补偿并不完美，有时甚至可能适得其反。例如，在你的脚受伤后，你可能会开始一瘸一拐，但不平衡的步态可能会导致背部疼痛。理解生物补偿何时以及如何适得其反，需要更好地理解补偿机制。我们将这个一般性问题应用到大脑如何保持稳定的神经元活动水平的问题上。这种稳定性很重要，因为大脑中的神经元都是相互连接的：当一个神经元发出电脉冲时，它会向其他神经元发送化学信号，这些信号要么刺激它们(使它们开火)，要么抑制它们(阻止它们放电)。如果兴奋和抑制变得不平衡，神经网络可能会失控，导致癫痫发作(过度兴奋)或沉默(过度抑制)。然而，随着我们根据感官经验进行学习，我们的大脑也在不断地变化。为了阻止这些变化不平衡的兴奋和抑制，大脑重新调整神经元及其连接，以补偿这些变化并恢复稳定的活动水平，这一过程被称为“自我平衡可塑性”。稳态可塑性的问题被认为是导致癫痫、自闭症和精神分裂症等疾病的原因。我们特别讨论了抑制性神经元的稳态可塑性。抑制性神经元应该如何补偿它们活动的变化？从整个网络的角度来看，你可能会认为，如果抑制性神经元过于活跃，那是因为它们被兴奋性神经元过度激活，所以抑制性神经元应该变得更加活跃，以便更好地压制它们的兴奋性邻居。我们称之为“以其他为中心”的可塑性，因为抑制性神经元主要关心稳定兴奋性神经元的活动。另一方面，更“自私”的抑制性神经元可能只会注意到自己过于活跃，因此会减少自己的活动，以回到自己喜欢的设定点。这种“以自我为中心”的补偿将在网络层面产生适得其反的效果，因为它们的兴奋性神经元受到的抑制甚至比平时更少，因此它们会变得更加活跃，这会恶化最初的问题。也就是说，在局部层面上有意义的补偿规则(它恢复了抑制性神经元的正常活动)适得其反，并且在网络层面上变得更糟(因为兴奋性神经元变得更加活跃)。令人惊讶的是，在不同的研究中观察到了抑制性神经元的自我中心补偿和他人中心补偿。目前尚不清楚其中一种或另一种发生在什么背景下，也不清楚是通过什么机制发生的。回答这些问题将有助于我们更好地理解神经网络如何维持正常的活动水平。我们将使用果蝇的嗅觉系统来研究这个问题，在这个系统中，一些抑制性神经元以自我为中心的方式进行补偿，而另一些则以其他中心的方式进行补偿。这种对比将让我们比较以自我为中心和以其他为中心的神经元如何影响它们的兴奋伙伴，以及控制可塑性的机制。首先，我们将确定哪些抑制性神经元使用自我中心和他人中心的补偿，以及它们在不同的背景下是否遵循不同的规则。然后，我们将检验我们的假设，即自我中心的抑制性补偿适得其反，并增加兴奋性神经元的活性。最后，我们将研究自我补偿与他人中心补偿背后的不同机制。特别是，我们假设，自我中心可塑性使用完全位于抑制性神经元内部的信号机制，而其他中心可塑性依赖于兴奋性神经元和抑制性神经元之间的相互作用(例如，兴奋性神经元“要求”抑制性神经元进行更多的抑制)。

One of the defining hallmarks of life is homeostasis: maintaining a constant state despite shifting conditions, by compensating for perturbations away from a desired set point. Yet this compensation is imperfect, and sometimes might even backfire. For example, after injuring your foot, you might start limping, but the imbalanced gait could then cause back pain. Understanding when and how biological compensation backfires requires a better understanding of compensatory mechanisms.We apply this general question to the problem of how the brain maintains stable levels of neuronal activity. This stability is important because neurons in the brain are all connected: when a neuron fires an electrical impulse, it sends chemical signals to other neurons that either excite them (make them fire) or inhibit them (stop them from firing). If excitation and inhibition become imbalanced, a neural network can spiral out of control into a seizure (too much excitation) or silence (too much inhibition). Yet our brains are constantly changing as we learn based on sensory experience. To stop these changes from unbalancing excitation and inhibition, the brain readjusts neurons and their connections to compensate for the changes and restore stable activity levels, a process called "homeostatic plasticity". Problems with homeostatic plasticity are thought to contribute to disorders like epilepsy, autism, and schizophrenia.We address in particular the homeostatic plasticity of inhibitory neurons. How should inhibitory neurons compensate for changes in their activity? Taking the perspective of the whole network, you might expect that if inhibitory neurons are too active, it's because they're overactivated by the excitatory neurons, so the inhibitory neurons should become even more active so as to better silence their excitatory neighbours. We call this "other-centred" plasticity because the inhibitory neurons mainly care about stabilising the activity of excitatory neurons. On the other hand, more "selfish" inhibitory neurons might notice only that they're too active, and therefore reduce their own activity to return to their preferred set point. This "self-centred" compensation would backfire at the network level, as their excitatory neurons would get even less inhibition than usual, so they would become more active, which worsens the original problem. That is, a compensation rule that makes sense at the local level (it restores the normal activity of the inhibitory neuron) is counterproductive and makes things worse at the network level (because the excitatory neurons become even more active).Surprisingly, both self-centred and other-centred compensation in inhibitory neurons have been observed in different studies. It's not clear in what contexts one or the other occurs, or by what mechanisms. Answering these questions will help us better understand how neural networks maintain normal activity levels.We'll study this problem using the olfactory system of the fruit fly Drosophila, where some inhibitory neurons compensate in a self-centred way, while others compensate in an other-centred way. This contrast will let us compare how the self-centred and other-centred neurons affect their excitatory partners and what mechanisms control the plasticity. First, we'll identify which inhibitory neurons use self-centred vs. other-centred compensation, and if they follow different rules in different contexts. Then, we'll test our hypothesis that self-centred inhibitory compensation backfires and increases activity of excitatory neurons. Finally, we'll investigate the different mechanisms underlying self- vs. other-centred compensation. In particular, we hypothesise that self-centred plasticity uses signalling mechanisms entirely internal to the inhibitory neuron, whereas other-centred plasticity relies on interactions between excitatory and inhibitory neurons (e.g., excitatory neurons "ask" inhibitory neurons for more inhibition).