Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.

中文翻译：

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反应性、主动性和诱导性代理：生物和人工尖峰网络的进化路径

复杂的环境提供结构化但可变的感官输入。为了最好地利用来自这些环境的信息，生物体必须进化出预测新刺激后果的能力，并根据这些预测采取行动。我们提出了神经网络的进化路径，引导有机体从反应行为到简单的主动行为，从简单的主动行为到基于感应的行为。基于早期的体外和计算机实验，我们定义了网络中具有尖峰时间依赖性可塑性的必要条件，以使生物体从被动行为转变为主动行为。我们的结果支持特定进化步骤的存在和体现神经网络从初始反应策略进化预测和归纳能力所必需的四个条件。