Neuromodulation and neuroplasticity work together to help organisms learn to cope in their environment. Experiments demonstrate that simple forms of neuromodulation aid in the learning process. In those studies, neuromodulation was used as a multiplier to scale the learning rate. However, more complex interactions have not been investigated. Our contributions are twofold: 1) we evolve a subnetwork that produces a modulatory signal that 2) is incorporated into the synaptic plasticity rule in nonlinear ways. In our experiments, we compute synaptic updates using a neural network and include the modulatory signal as one of the inputs. This allows evolution to combine the synaptic activity with modulation in highly nonlinear ways to arrive at weight updates. We show that organisms that evolve with this added complexity outperform simpler multiplicative neuromodulation, suggesting that gains might be won by investigating a broader class of interactions between neuromodulation and synaptic plasticity.

中文翻译：

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进化广义调制学习：统一神经调制和突触可塑性

神经调节和神经可塑性共同作用，帮助生物体学习如何应对环境。实验表明，简单形式的神经调节有助于学习过程。在这些研究中，神经调节被用作衡量学习率的乘数。然而，尚未研究更复杂的相互作用。我们的贡献是双重的：1）我们发展了一个产生调制信号的子网络，2）以非线性方式纳入突触可塑性规则。在我们的实验中，我们使用神经网络计算突触更新，并将调制信号作为输入之一。这允许进化以高度非线性的方式将突触活动与调制相结合，以达到权重更新。