Plasticity of inhibitory synaptic transmission in the hippocampus

大脑的组成部分是神经细胞，也被称为神经元，它们通过突触相互连接。神经元分为两类：兴奋性神经元促进与其相连的神经元的活动，而抑制性神经元则抑制活动。尽管抑制性神经元的数量少于兴奋性神经元（20%），但它们在大脑功能和计算中起着核心作用。抑制性神经元的破坏会导致兴奋和抑制失衡，进而导致癫痫、精神分裂症和自闭症谱系障碍等疾病。因此，了解是什么调节兴奋性和抑制性突触的强度是一个重要的研究目标。有趣的是，尽管人们对兴奋性突触的可塑性有很多关注，但抑制突触的可塑性却在很大程度上被忽视了。在这个BBSRC项目中，我们旨在研究抑制性突触可塑性的现象学和功能。海马体是一个在情景记忆和空间导航中起重要作用的大脑区域。海马体中的神经网络活动表现出多种离散状态，这些状态由节律性振荡识别，并执行不同的计算功能。在探索期间，当θ (5-12Hz)和γ (30-120Hz)频率振荡普遍存在时，记忆最初被编码，而记忆的巩固在休息或睡眠期间的短暂尖锐波纹波（~200Hz）活动期间离线发生。抑制神经元的特定亚型已被发现在这些不同的行为状态下是活跃的，有趣的是，抑制可塑性已被证明依赖于抑制性和兴奋性神经元放电的精确协调以及神经网络的状态。这表明不同的行为状态可能在特定的抑制性突触上触发不同形式的抑制性可塑性。然而，关于海马体中抑制性可塑性的作用的证据非常有限。我们的假设是抑制性突触的可塑性：a)可以调节网络计算，b)特定于抑制性神经元亚型，c)以行为状态依赖的方式决定兴奋性神经元的输入选择性。我们建议一个实验团队与一个最近提出抑制可塑性理论模型的计算团队共同努力，该团队在海马体突触可塑性方面具有专业知识。通过一个紧密的相互作用，允许计算模型被告知并指导实验和实验来完善模型，我们的目标是研究神经活动如何参与抑制性可塑性，这取决于抑制性神经元亚型，以及抑制性可塑性如何影响海马中的网络计算。这项工作将为抑制可塑性的机制和功能提供重要信息，最终将提高我们对疾病状态下抑制如何改变的理解，从而可能导致新的治疗方法。

The building blocks of the brain are nerves cells, also called neurons, connected to each other by synapses. Neurons form two categories: excitatory neurons promote activity in their connected neurons, whereas inhibitory neurons depress activity. Even though inhibitory neurons are less numerous than excitatory neurons (20%), they play a central role in brain function and computation. Disruption of inhibitory neurons leads to an imbalance in excitation and inhibition that can, in turn, lead to diseases such as epilepsy, schizophrenia and autism spectrum disorders. Thus, understanding what regulates the strength of excitatory and inhibitory synapses is an important research goal. Interestingly, although there has been much focus on plasticity of excitatory synapses, plasticity of inhibitory synapses has been largely neglected. In this BBSRC project, we aim to investigate the phenomenology and function of inhibitory synaptic plasticity. The hippocampus is a brain area that plays an important role in episodic memory and spatial navigation. Neuronal network activity in the hippocampus exhibits multiple discrete states that are identified by rhythmic oscillations and perform distinct computational functions. Memories are initially encoded during exploration when theta (5-12Hz) and gamma (30-120Hz) frequency oscillations are prevalent whereas consolidation of memories occurs offline in periods of transient sharp wave ripple (~200Hz) activity during rest or sleep. Specific subtypes of inhibitory neurons have been found to be active in these distinct behavioural states and, intriguingly, inhibitory plasticity has been shown to depend on the precise co-ordination of inhibitory and excitatory neuron firing and on the state of the neural network. This indicates that different behavioural states are likely to trigger distinct forms of inhibitory plasticity at specific inhibitory synapses. However, there is very limited evidence for the role of inhibitory plasticity in the hippocampus.Our hypotheses are that plasticity of inhibitory synapses: a) can regulate network computations, b) is specific to the inhibitory neurons subtypes and c) determines input selectivity for excitatory neurons in a behavioural state dependent manner. We propose a joint effort from an experimental team, which has expertise in synaptic plasticity in the hippocampus, with a computational team that recently proposed a theoretical model of inhibitory plasticity. By a tight interaction allowing the computational model to be informed by and to guide experiments and experiments to refine the model, we aim to investigate how neural activity engages inhibitory plasticity depending on the inhibitory neuron subtype, and how inhibitory plasticity shapes network computations in the hippocampus. This work will provide important information on the mechanisms and function of inhibitory plasticity that will ultimately improve our understanding of how inhibition may be modified in disease states potentially leading to new therapies.