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首页> 外文会议>Engineering applications of bio-inspired artificial neural networks >Challenges for a Real-World Information Processing by Means of Real-Time Neural Computation and Real-Conditions Simulation
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Challenges for a Real-World Information Processing by Means of Real-Time Neural Computation and Real-Conditions Simulation

机译：实时神经计算和真实条件仿真对现实世界信息处理的挑战

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Should we consider the dimensions of natural neural computation as they are known as a result of the scientific research, we realize there is a long tomorrow before us, interested in neural computation, for the simple reason that we can only handle a relatively low number of units and connections nowadays. All along this century we have significantly improved our knowledge on natural neural nets, to realize that huge number of cells and connections and begin to understand some of the brain signals processing and the repetitive structures which support it. However, even in the most developed cases, such as the auditory pathway modelling, there is not a neural computational device which can involve a real time response and follow the facts already known or plausibly postulated on some brain processes (e.g. by McCulloch and Pitts), with the unavoidable great number of processing elements involved too, besides neither suitable models regarding those kind of real-look nets have been designed nor their corresponding real-conditions simulations have been carried out. That means there is a lack of connectionistically computable models and also reduction methods by which we can obtain a connectionistic implementation design, given the knowledge level model.
机译：如果我们考虑一下自然神经计算的维度（由于科学研究而闻名），我们意识到对神经计算感兴趣的还有一个漫长的明天，因为我们只能处理相对较少的数量，这很简单。如今的单位和联系。在整个世纪中，我们已经大大提高了对自然神经网络的了解，认识到大量的细胞和连接，并开始了解某些大脑信号处理和支持它的重复结构。但是，即使在最发达的情况下，例如听觉路径建模，也没有神经计算设备可以涉及实时响应并遵循某些大脑过程中已知或合理假设的事实（例如，McCulloch和Pitts）除了不可避免地涉及大量处理元素外，除了没有设计出关于那种真实外观网络的合适模型，也没有进行相应的真实情况模拟。这意味着缺乏连接性可计算模型，并且缺乏简化方法，在给定知识水平模型的情况下，我们可以通过该方法来获得连接性实现设计。

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