研究人工认知系统中认知过程的模拟系统——激进联结主义和计算神经认识论

M.F. Peschl
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引用次数: 5

摘要

所述项目的目的是实现对认知过程的更深层次的理解。它基于认知是发生在自然或人工神经网络(ANN)中的神经活动的结果的假设。在所提出的模型中,网络不是嵌入到语言环境中,而是通过传感器和效应器与环境物理耦合。从认识论和计算机科学的角度来看,这是一个激进的步骤,有许多非常重要的含义。在计算神经认识论中,这种连接主义被称为激进连接主义或激进神经计算。人工神经网络必须在物理上嵌入到它的环境中。这意味着系统与其环境之间的通信是通过效应器和传感器进行的。在这个互动过程中不涉及任何符号。需要一个循环的拓扑结构来保证非线性和非平凡的行为。给出了环境的模拟、人工认知系统与环境之间的相互作用以及模拟的实现的技术细节
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A simulation system for the investigation of cognitive processes in artificial cognitive systems-Radical connectionism and computational neuroepistemology
The aim of the project described is to achieve a deeper understanding of cognitive processes. It is based on the assumption that cognition is the result of neural activities taking place in a natural or artificial neural network (ANN). In the model presented the network is not embedded into a linguistic environment but rather is physically coupled to the environment via sensors and effectors. From an epistemological as well as computer science perspective this is a radical step which has many very important implications. In computational neuroepistemology this kind of connectionism is called radical connectionism or radical neural computing. The ANN has to be physically embedded into its environment. This means that the communication between the system and its environment takes place via effectors and sensors. No symbols are involved in this process of interaction. A recurrent topology is required which ensures a nonlinear and nontrivial behavior. Technical details are given on the simulation of the environment, of the interactions between the artificial cognitive system(s) and the environment and on the implementation of the simulation.<>
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Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
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