Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
Volen Center and Biology Department, Brandeis University, Waltham, Massachusetts 02454.
J Neurosci. 2021 Mar 3;41(9):1850-1863. doi: 10.1523/JNEUROSCI.1719-20.2020. Epub 2021 Jan 15.
Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analog signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing. Understanding how neurons process information is a fundamental question in neuroscience. Purkinje cells (PCs) were traditionally regarded as linear point neurons. We used computational modeling to unveil their electrophysiological properties underlying the multiplexed coding strategy that is observed during behaviors. We demonstrate that increasing input intensity triggers localized dendritic spikes, shifting PCs from linear rate-coders to burst-pause timing-coders. Both coding strategies work at the level of individual dendritic branches. Our work suggests that PCs have the ability to implement branch-specific multiplexed coding at the cellular level, thereby increasing the capacity of cerebellar coding and learning.
神经元的发放模式对于支持电路水平的行为至关重要。在小脑浦肯野细胞（PC）中，尖峰率和停顿都被用于行为编码，但导致编码转换的细胞机制尚不清楚。我们使用经过充分验证的 PC 模型来探索单个 PC 用于处理平行纤维（PF）输入的编码策略。我们发现，随着输入强度的增加，通过触发局部树突棘，PC 从线性率编码器转变为爆发停顿定时编码器。我们使用实验数据验证了树突棘的特性，阐明了尖峰机制，并预测了有和没有抑制时的尖峰阈值。线性和爆发停顿计算都使用单个分支作为计算单元，这挑战了 PC 作为线性点神经元的传统观点。树突棘阈值可以通过电压状态、分支间的相互作用和突触抑制来调节，以扩展线性计算或爆发停顿计算的动态范围。此外，分支之间的共同激活 PF 输入可以改变体最大尖峰率和停顿持续时间，使其携带模拟信号。我们的结果为单个神经元用于扩展其信息处理能力的策略提供了新的见解。理解神经元如何处理信息是神经科学中的一个基本问题。浦肯野细胞（PC）传统上被认为是线性点神经元。我们使用计算建模来揭示它们在行为过程中观察到的多路复用编码策略的电生理特性。我们证明，增加输入强度会触发局部树突棘，从而将 PC 从线性率编码器转换为爆发停顿定时编码器。这两种编码策略都在单个树突分支的水平上起作用。我们的工作表明，PC 具有在细胞水平上实现分支特异性多路复用编码的能力，从而增加了小脑编码和学习的能力。
