基于大规模脉冲网络的神经元时序活动研究

Temporal Activity of Neurons Based on Large-Scale Pulse Networks

摘要: 为了揭示小脑定时学习的功能特性，需要弄清楚神经元是如何在大规模网络中相互作用和协调的。为此，本文研究了小脑中的神经元有序的脉冲发射活动会否受到神经元之间突触连接方式的影响。延迟眨眼条件反射的仿真实验结果表明参与条件反射(CR)的神经元网络产生了顺序活动，而有序的行动序列是任何有意义行为的关键，因此本实验考虑了将模型中的所有颗粒细胞以不等概率向所有方向投射的情况，通过模拟二维柏林噪声的方式改变原有的随机循环连接，最后的仿真结果表明早期脉冲的发射时间延后，说明小脑中神经元的时序活动的发生可能归因于它们的外部输入。

Abstract: In order to reveal the functional properties of cerebellar timing learning, it is necessary to understand how neurons interact and coordinate in large-scale networks. To this end, we investigated whether the ordered pulse firing activity of neurons in the cerebellum is affected by the synaptic connections between neurons. The simulation results of delayed blink conditioned reflex show that the neural network involved in conditioned reflex (CR) generates sequential activity, and the ordered sequence of actions is the key to any meaningful behavior. Therefore, this experiment considers the case of projecting all granular cells in the model in all directions with unequal probability. By modifying the original random loop connections by simulating 2D Berlin noise, the final simulation results show that the firing time of the early pulses is delayed, suggesting that the timing activity of neurons in the cerebellum may be attributed to their external input.