About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin’s studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.

中文翻译：

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神经网络的稀疏性与能量消耗的关系

大约 50-80% 的总能量被神经网络中的信号消耗。如果网络中有许多活跃的神经元，则神经网络会消耗大量能量。如果神经网络中的活跃神经元很少，则网络消耗的能量非常少。神经网络的活跃神经元与所有神经元的比率，即稀疏度，影响神经网络的能量消耗。Laughlin 的研究表明，节能代码的稀疏性取决于信令和固定成本之间的平衡。Laughlin 没有给出信号与固定成本的确切比率，也没有给出大多数节能神经网络中活跃神经元与所有神经元的比率。在本文中，我们通过生理学实验的数据计算了信号成本与固定成本的比率。信令成本与固定成本的比率在 1.3 和 2.1 之间。我们计算了大多数节能神经网络中活跃神经元与所有神经元的比率。神经网络中活跃神经元与所有神经元的比率在 0.3 到 0.4 之间。我们的结果与许多相关生理实验的数据一致，表明本文使用的模型可能满足真实条件下的神经编码。本文的计算结果可能有助于神经编码的研究。表明本文使用的模型可能满足真实条件下的神经编码。本文的计算结果可能有助于神经编码的研究。表明本文使用的模型可能满足真实条件下的神经编码。本文的计算结果可能有助于神经编码的研究。