In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.

中文翻译：

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用于模拟听觉感觉细胞和突触的卷积神经网络框架

在经典的计算神经科学中，分析模型描述源自神经元记录以模拟潜在的生物系统。这些神经元模型通常计算速度较慢，并且无法集成到大规模神经元模拟框架中。我们提出了一种混合的、机器学习和计算神经科学方法，将感觉神经元和突触的分析模型转换为具有相同生物物理特性的深度神经网络 (DNN) 神经元单元。我们的 DNN 模型架构包含并行和可微方程，可用于神经工程应用中的反向传播，并在 CPU 或 GPU 系统上分别提供 70 和 280 的模拟运行时间改进因子。我们专注于听觉神经元和突触的发展，并表明我们的 DNN 模型架构可以扩展到各种现有的分析模型。我们描述了我们的听觉模型方法如何应用于其他神经元和突触类型，以帮助加速大规模大脑网络的发展和基于 DNN 的病理系统治疗。