Mattia M, Del Giudice P
Physics Laboratory, Istituto Superiore di Sanità, Roma, Italy.
Neural Comput. 2000 Oct;12(10):2305-29. doi: 10.1162/089976600300014953.
A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6% more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm. We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.
本文描述了一种模拟程序，用于对具有脉冲发放神经元和可塑性突触的递归神经网络进行大规模模拟。如果神经元和突触的动态变量在任意两个连续脉冲之间确定性地演化，那么该程序是适用的。脉冲会使这些变量产生跳跃，并且由于脉冲序列通常是有噪声的，所以脉冲会给动力学引入随机性。由于模拟中的所有事件都是由神经元或突触处的脉冲到达所引导的，我们将此程序称为事件驱动。详细描述了该程序，并将其逻辑和性能与传统（同步）模拟进行了比较。新方法的主要影响是，在引入动态突触效能时，计算负荷大幅降低，动态突触效能会根据突触前和突触后神经元的活动而有机变化。事实上，在存在突触动力学的情况下，每个神经元的计算负荷随神经元数量线性增长，仅比固定突触时的负荷高约6%。即使是后者，该算法也能相当高效地处理。我们在一个特定案例中展示了该算法的运行情况，该案例使用积分发放神经元和特定的脉冲驱动突触动力学。已发现这两种动态元件都可在超大规模集成电路中自然实现。对该网络进行模拟，以展示刺激呈现对突触结构的影响，以及生成矩阵对其所诱导的神经活动的稳定性。
