The study of biorealistic neuron circuits has been limited by the efficiency of digital implementations. Efficient digital approaches generally use I&F variants, losing important neural properties for network computation. In contrast, accurate neuron ODEs tend to utilize computationally intensive operations, causing the overhead to become prohibitive for large spiking neural network applications. This effort presents efficient digital approximations for coupled HH neurons derived from transistor-channel neural modeling. Neuron models are implemented in C using floating-point and 32-bit fixed-point arithmetic, and small networks are simulated using a fixed-step ODE solver. Our approach enables large network simulation of HH-like neurons, facilitating scalable digital modeling while also providing a direct path towards a framework for analog computation.

中文翻译：

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迈向生物真实硅神经元网络的可扩展数字建模

生物现实神经元电路的研究受到数字实现效率的限制。高效的数字方法通常使用 I&F 变体，从而丢失了网络计算的重要神经属性。相比之下，精确的神经元常微分方程倾向于利用计算密集型运算，导致开销对于大型尖峰神经网络应用来说变得令人望而却步。这项工作为源自晶体管通道神经建模的耦合 HH 神经元提供了有效的数字近似。神经元模型使用浮点和 32 位定点算法在 C 中实现，小型网络使用固定步长 ODE 求解器进行模拟。我们的方法能够实现 HH 类神经元的大型网络模拟，促进可扩展的数字建模，同时还提供通往模拟计算框架的直接路径。