Department of Computer Science, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus.
Brain Res. 2012 Jan 24;1434:115-22. doi: 10.1016/j.brainres.2011.07.025. Epub 2011 Jul 22.
In a network of leaky integrate-and-fire (LIF) neurons, we investigate the functional role of irregular spiking at high rates. Irregular spiking is produced by either employing the partial somatic reset mechanism on every LIF neuron of the network or by using temporally correlated inputs. In both the benchmark problem of XOR (exclusive-OR) and in a general-sum game, it is shown that irrespective of the mechanism that is used to produce it, high firing irregularity enhances the learning capability of the spiking neural network trained with reward-modulated spike-timing-dependent plasticity. These results suggest that the brain may be utilising high firing irregularity for the purposes of learning optimisation.
在一个漏积分和放电 (LIF) 神经元网络中，我们研究了高速率不规则尖峰放电的功能作用。不规则尖峰放电可以通过对网络中的每个 LIF 神经元采用部分胞体重置机制，或者通过使用时间相关的输入来产生。在异或 (XOR) 基准问题和一般和问题中，结果表明，无论使用哪种机制来产生不规则尖峰放电，高频率的不规则尖峰放电都可以增强使用奖励调节的尖峰时间依赖可塑性进行训练的脉冲神经网络的学习能力。这些结果表明，大脑可能利用高频率的不规则尖峰放电来进行学习优化。
