Di Sia Paolo, Licata Ignazio
Department of Philosophy, Education and Psychology, University of Verona, Lungadige Porta Vittoria 17, Verona 37129, Italy.
ISEM, Institute for Scientific Methodology, Palermo 90146, Italy.
Bioengineering (Basel). 2016 Apr 5;3(2):11. doi: 10.3390/bioengineering3020011.
The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in the inner brain parameters, in order to mimic the external transformation by the proper use of sensors and effectors. This paper highlights some crossing research areas of natural computing, nanotechnology, and brain modeling and considers two interesting theoretical approaches related to brain dynamics: (a) the memory in neural network, not as a passive element for storing information, but integrated in the neural parameters as synaptic conductances; and (b) a new transport model based on analytical expressions of the most important transport parameters, which works from sub-pico-level to macro-level, able both to understand existing data and to give new predictions. Complex biological systems are highly dependent on the context, which suggests a "more nature-oriented" computational philosophy.
目前，脑动力学研究利用了纳米生物技术和生物工程的新特性。新的几何和分析方法在所有科学领域都显得非常有前景，尤其是在脑过程研究中。深入理解的努力会导致脑内部参数的变化，以便通过合理使用传感器和效应器来模拟外部转变。本文重点介绍了自然计算、纳米技术和脑建模的一些交叉研究领域，并考虑了与脑动力学相关的两种有趣的理论方法：(a) 神经网络中的记忆，并非作为存储信息的被动元素，而是作为突触电导整合在神经参数中；(b) 一种基于最重要传输参数解析表达式的新传输模型，其作用范围从亚皮秒级到宏观级，既能理解现有数据，又能做出新的预测。复杂的生物系统高度依赖于环境，这暗示了一种“更以自然为导向”的计算理念。
