The question how neural systems (of humans) can perform reasoning is still far from being solved. We posit that the process of forming Concepts is a fundamental step required for this. We argue that, first, Concepts are formed as closed representations, which are then consolidated by relating them to each other. Here we present a model system (agent) with a small neural network that uses realistic learning rules and receives only feedback from the environment in which the agent performs virtual actions. First, the actions of the agent are reflexive. In the process of learning, statistical regularities in the input lead to the formation of neuronal pools representing relations between the entities observed by the agent from its artificial world. This information then influences the behavior of the agent via feedback connections replacing the initial reflex by an action driven by these relational representations. We hypothesize that the neuronal pools representing relational information can be considered as primordial Concepts, which may in a similar way be present in some pre-linguistic animals, too. We argue that systems such as this can help formalizing the discussion about what constitutes Concepts and serve as a starting point for constructing artificial cogitating systems.
翻译:神经系统(人类的神经系统)如何进行推理的问题还远没有解决。 我们假设,形成概念的过程是这个过程所需要的一个基本步骤。 我们争论说,首先,概念是作为封闭式陈述形成的,然后通过将它们相互联系起来加以巩固。 我们在这里提出了一个模型系统(代理),它有一个小型神经网络,使用现实的学习规则,并且只从代理实施虚拟行动的环境得到反馈。首先,该代理器的行动是反射的。在学习过程中,输入中的统计规律导致形成神经库,代表该代理商从人为世界观察到的实体之间的关系。这种信息随后通过反馈连接影响代理人的行为,以这些关联性陈述所驱动的行动取代最初的反射。我们假设,代表关系信息的神经库可以被视为原始概念,在某些语言前的动物中也可能存在类似的情况。我们争论说,这种系统有助于正式讨论概念的构成要素,并成为建造人工合成系统的一个起点。