Ros Eduardo, Carrillo Richard, Ortigosa Eva M, Barbour Boris, Agís Rodrigo
Neural Comput. 2006 Dec;18(12):2959-93. doi: 10.1162/neco.2006.18.12.2959.
Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.
大脑中几乎所有的神经元信息处理和神经元间通信都涉及动作电位，即尖峰信号，它不仅驱动神经元的短期突触动力学，还通过突触可塑性驱动其长期动力学。在许多脑结构中，动作电位活动被认为是稀疏的。通过开发事件驱动模拟方案，这种活动的稀疏性已被用于降低大规模网络模拟的计算成本。然而，现有的事件驱动模拟方案使用的是极其简化的神经元模型。在这里，我们实现并批判性地评估了一种事件驱动算法（ED-LUT），该算法使用预先计算的查找表来表征突触和神经元动力学。这种方法能够在表示神经元时使用更复杂（且更现实）的神经元模型或数据，同时保留高速模拟的优势。我们展示了该方法在包含指数突触电导的神经元中的应用，从而实现了分流抑制，这是一种对细胞计算至关重要的现象。我们还引入了一种改进的两阶段事件队列算法，该算法允许模拟有效地扩展到具有任意传播延迟的高度连接网络。最后，该方案很容易适应依赖于尖峰时间的突触可塑性机制的实现，使未来的模拟能够探索大规模网络中的长期学习和适应问题。
