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Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

机译：分布式小脑可塑性在操作任务中实现自适应增益控制：闭环机器人仿真

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Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, ). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., ; Gao et al., ) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario.
机译：自适应增益调节是由小脑-小脑回路执行的前向控制器操作的核心，它允许以预测的方式对电动机动作的强度进行微调。为了学习和存储有关人体动态的信息并生成内部运动模型，小脑被认为具有长期的突触可塑性。 PF-PC突触处的LTD通常被假定具有该功能（Marr，）。然而，仅这种可塑性不能解释小脑适应的广泛动态范围和时间尺度。因此，我们通过生成嵌入到连接到机器人模拟器的控制环中的模拟小脑模型，来测试分布在多个突触部位的可塑性的作用（Hansel等； Gao等）。该机器人使用了三关节手臂，并沿着8形轨迹以不同的质量重复执行了快速操作。根据生物学证据，小脑模型在PF-PC，MF-DCN和PC-DCN突触处均具有LTD和LTP。与仅包含PF-PC突触可塑性的网络方案相比，这种网络方案的有效性大大扩展。的确，包括分布式可塑性的系统能够可靠地自适应，以按照行为测试中观察到的方法，在包括快速学习和巩固的时间过程中，自适应地操纵不同的质量并学习手臂-物体的动力学。尤其是，PF-PC可塑性在实际输入状态和系统误差之间充当时间相关器，而MF-DCN和PC-DCN可塑性在生成增益控制器中起着关键作用。该模型表明，分布的突触可塑性允许产生小脑的复杂学习特性。进一步的可塑性机制和尖峰信号处理的结合将使该概念在更现实的计算场景中得到扩展。

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