Carrillo Richard R, Ros Eduardo, Boucheny Christian, Coenen Olivier J-M D
Department of Computer Architecture and Technology, ETSI Informática y de Telecomunicación, University of Granada, Spain.
Biosystems. 2008 Oct-Nov;94(1-2):18-27. doi: 10.1016/j.biosystems.2008.05.008. Epub 2008 Jun 20.
We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts.
我们描述了一种基于具有基于电导的突触的积分发放脉冲神经元的小脑神经网络模型。神经元特性源自我们早期对不同小脑神经元的详细模型。我们在一个带有机器人平台的实时控制应用中测试了小脑模型。根据生物系统在不同的感觉运动通路中引入了延迟。小脑模型中的主要可塑性是平行纤维到浦肯野细胞连接的脉冲时间依赖可塑性（STDP）。这种STDP由下橄榄核（IO）活动驱动，该活动使用一种新颖的概率低频模型对误差信号进行编码。我们在一个使用目标到达任务的机器人控制系统中展示了小脑模型。我们测试该系统是否学会以非破坏性方式到达不同的目标位置，从而提取一个通用动力学模型。为了测试系统自适应不同动态情况的能力，我们展示了在显著改变机器人平台动力学（其摩擦力和负载）后获得的结果。实验结果表明，基于小脑的系统能够动态适应不同的情境。
