The evolution of symbolic communication is a longstanding open research question in biology. While some theories suggest that it originated from sub-symbolic communication (i.e., iconic or indexical), little experimental evidence exists on how organisms can actually evolve to define a shared set of symbols with unique interpretable meaning, thus being capable of encoding and decoding discrete information. Here, we use a simple synthetic model composed of sender and receiver agents controlled by Continuous-Time Recurrent Neural Networks, which are optimized by means of neuro-evolution. We characterize signal decoding as either regression or classification, with limited and unlimited signal amplitude. First, we show how this choice affects the complexity of the evolutionary search, and leads to different levels of generalization. We then assess the effect of noise, and test the evolved signaling system in a referential game. In various settings, we observe agents evolving to share a dictionary of symbols, with each symbol spontaneously associated to a 1-D unique signal. Finally, we analyze the constellation of signals associated to the evolved signaling systems and note that in most cases these resemble a Pulse Amplitude Modulation system.

翻译：象征性通信的演进是生物学中长期的开放研究问题。虽然一些理论认为信号的解码来自亚符号通信(即图标或索引学),但几乎没有实验性证据表明生物体如何实际演变,以定义一套具有独特解释意义的共同符号,从而能够对离散信息进行编码和解码。在这里,我们使用由连续时经常神经网络控制的发件和接收物剂组成的简单合成模型,该模型以神经进化为手段进行优化。我们把信号解码描述为倒退或分类,以有限和无限的信号扩增。首先,我们展示这种选择如何影响进化搜索的复杂性,并导致不同程度的概括化。然后我们评估噪音的影响,并在一个优惠游戏中测试进化的信号系统。在各种环境下,我们观察物剂演变成一个符号词典,每个符号都自动与一个1D独特的信号联系起来。最后,我们分析与进化信号系统相关的信号星座,并指出在大多数情况下这些信号类似于脉冲模模系统。