Rotation 1: Robust and efficient spiking computations

BBSRC的战略主题：理解生命的规则神经功能的经典观点依赖于兴奋和抑制之间的微妙平衡，前者驱动尖峰，后者抑制尖峰。然而，越来越多的证据表明，这个框架过度简化了支配尖峰产生的复杂动态。例如，海德尔等人。(2013)揭示了在清醒的动物中，抑制主导皮质反应，挑战了简单的兴奋-抑制平衡的概念。此外，Guo等人还提出了一些新的结论。(2015)证明了抑制可以通过小鼠运动皮质中的反弹电流反常地触发运动，这呼应了长期以来对运动基础的中央模式发生器电路的研究(Marder&Calabrese 1996)，其中抑制明显地导致具有时间延迟的峰电位。这些观察提出了一个基本问题：如果不是通过兴奋和抑制之间的直接、瞬时竞争，突触输入如何影响峰电位？答案可能在于电压门控(本征)电导，它将信号从树突传播到轴突，但包括各种不同的离子选择性、通道动力学和反转电位。我的初步建模工作利用本征电流来复制灵长类动物，通过将电机准备投射为反弹电流的初始化。事实上，通过将树突电流与棘波产生分离，所有突触输入本质上都必须是预备性的，为未来的行为配置内在电导。在这个框架下，准备可以被视为整个大脑神经元功能的基本属性，而不是运动皮质的利基人工制品。这与最近的研究结果一致，即桶状皮质在参与复杂的感觉辨别任务之前显示出准备的迹象(君士坦丁堡和布鲁诺，2013年；Park等人。2022年；罗杰斯等人。2021年)。这些趋同的证据指向另一种假说：兴奋和抑制相互作用，精确地控制依赖电压的细胞内过程，进而控制神经元的输出。作为推论，动作电位的短暂时间尺度可能允许神经元在不中断这些较慢的电压依赖过程的情况下进行数字通信。为了探索这些想法，我提出了一个多方面的方法。我将开发和分析一个多隔室神经元模型，该模型通过一组电压门控电导将突触输入与棘波产生分离。这将为研究神经元“内在准备”的机制提供一个平台。我将训练这些神经元的网络来执行自然主义的感觉和运动任务，例如感觉辨别(Rodgers等人。2021年)和化合物到达(Zimnik等人2021年)。这将使我能够评估作为神经元计算的一个基本方面的准备的一般性。使用训练好的网络，我将生成关于树突和轴突中电压动态的可测试预测。这些预测将形成与使用电压成像技术的实验小组合作的基础(Wong-Campos等人。2023)，促进理论和实验之间的对话。通过重新构建兴奋-抑制相互作用并研究“内在准备”的作用，该项目旨在为神经元计算的原理提供新的线索。所获得的见解可能对我们理解大脑如何处理信息和产生复杂行为具有深远影响，可能会启发人工智能和神经形态计算的新方法。

BBSRC strategic theme: Understanding the rules of lifeThe classical view of neuronal function relies on a delicate balance between excitation and inhibition, with the former driving spiking and the latter suppressing it. However, growing evidence suggests that this framework oversimplifies the complex dynamics governing spike generation. For instance, Haider et al. (2013) revealed that inhibition dominates cortical responses in awake animals, challenging the notion of a simple excitatory-inhibitory balance. Furthermore, Guo et al. (2015) demonstrated that inhibition can paradoxically trigger movement through rebound currents in the mouse motor cortex, echoing long-standing studies in the central pattern generator circuits underlying locomotion (Marder & Calabrese 1996) where inhibition explicitly causes spikes with a temporal delay.These observations raise a fundamental question: how do synaptic inputs influence spiking if not through a direct, instantaneous competition between excitation and inhibition? The answer may lie with voltage-gated (intrinsic) conductances, which propagate signals from dendrites to the axon yet comprise a huge diversity of different ion selectivities, channel kinetics, and reversal potentials. My preliminary modelling work leverages an intrinsic current to reproduce primate reaches, by casting motor preparation as the initialisation of a rebound current. Indeed by decoupling dendritic currents from spike-generation, all synaptic inputs must be preparatory in nature, configuring intrinsic conductances for future behaviour. Under this framework, preparation can be seen as a fundamental property of neuronal function across the brain rather than a niche artefact of the motor cortex. This aligns with recent findings that the barrel cortex exhibits signs of preparation before engagement in a complex sensory discrimination task (Constantinople and Bruno 2013; Park et al. 2022; Rodgers et al. 2021). These converging lines of evidence point towards an alternative hypothesis: excitation and inhibition cooperate to precisely control voltage-dependent intracellular processes, which in turn govern neuronal output. As a corollary, the brief timescale of the action potential may allow neurons to communicate digitally without disrupting these slower voltage-dependent processes. To explore these ideas, I propose a multi-faceted approach. I will develop and analyse a multi-compartment neuron model that decouples synaptic inputs from spike generation through a set of voltage-gated conductances. This will provide a platform for investigating the mechanisms underlying "intrinsic preparation" of neurons. I will train a network of these neurons to perform naturalistic sensory and motor tasks, such as sensory discrimination (Rodgers et al. 2021) and compound reaching (Zimnik et al. 2021). This will allow me to assess the generality of preparation as a fundamental aspect of neuronal computation. Using the trained network, I will generate testable predictions about voltage dynamics in dendrites and axons. These predictions will form the basis for collaborations with experimental groups employing voltage imaging techniques (Wong-Campos et al. 2023), facilitating a dialogue between theory and experiment.By reframing the excitatory-inhibitory interplay and investigating the role of "intrinsic preparation", this project aims to shed new light on the principles governing neuronal computation. The insights gained may have far-reaching implications for our understanding of how the brain processes information and generates complex behaviours, potentially inspiring novel approaches in artificial intelligence and neuromorphic computing.