This paper suggests a statistical framework for describing the relations between the physical and conceptual entities of a brain-like model. In particular, features and concept instances are put into context. This may help with understanding or implementing a similar model. The paper suggests that features are in fact the wiring. With this idea, the actual length of the connection is important, because it is related to firing rates and neuron synchronization. The paper then suggests that concepts are neuron groups that link features and concept instances are the signals from those groups. Therefore, features become the static framework of the interconnected neural system and concepts are combinations of these, as determined by the external stimulus and the neural synaptic strengths. Along with this statistical model, it is possible to propose a simplified design for a neuron, based on an action potential and variable output signal. A strong comparison with Hebbian theory is then proposed, with some test results to support the theory.
翻译:本文建议了一个统计框架,用于描述类似大脑模型物理和概念实体之间的关系。 特别是, 将特征和概念实例放在上下文中。 这可能有助于理解或实施类似的模型。 论文指出, 特征实际上是电线。 有了这个想法, 连接的实际长度很重要, 因为它与燃烧率和神经同步有关。 本文然后建议, 概念是神经神经组, 将特征和概念实例联系起来, 是这些组的信号。 因此, 特征成了相互关联的神经系统的静态框架, 概念是这些概念的组合, 由外部刺激和神经合成力量决定。 与这一统计模型一起, 可以提出一个基于行动潜力和可变输出信号的神经元简化设计。 然后建议与赫比理论进行有力的比较, 并有一些测试结果支持理论。