Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification

R. Koene, Diana Deca
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引用次数: 4

Abstract

Whole brain emulation (WBE) is a systematic approach to large-scale neuroprostheses with theintent to replicate the functions of a specific mind in some other operating substrate. The engineeringpractice of system identification can be applied in a way that makes this big problem a feasiblecollection of connected smaller system identification problems to solve.Whole brain emulation is an essential goal for neuroscience. Following Richard Feynman’sfamous 1988 Caltech chalkboard quote: “What I cannot create, I do not understand.” To create orbuild a human mind we need models, a combination of building blocks with processes. When weexplain something that is observed, e.g., mental functions and behaviors, we strive to make thatpredictable within constraints that satisfy our interests: We create boundaries, we measure withinthose well-defined outlines, and then we use those measurements to derive model processes enablingoutcome prediction. Within the defined system outlines of our model, taking into account definedsets of signals, we mathematically describe interactions (which may be expressed in informationtheoretic terms).Every aspect of modern science relies on creating representations of things. In each case, wefocus on the signals and the observables (or behavior) that interest us. Then, we try to interpret interms of functions what the system processes are doing. Where brain functions are concerned, somecognitive prosthetic work, such as the pioneering efforts of the labs of Theodore W. Berger at theUniversity of Southern California, has managed to carry out these steps and produced successfulexperimental results (Berger et al., 2012). Berger’s team has developed and tested an experimentalhippocampal neural prosthetic that is implemented on a bio-mimetic chip. A transfer functionwas identified and used to replicate the operational properties of biological neural circuitry in aregion of the rat hippocampus known as CA3. In experiments, the prosthesis is able to reproducethe way in which input to the region is turned into output from that region. This method ofdeveloping neuroprostheses, with demonstrated success in rats, is presently being tested in primates(Marmarelis et al., 2013).
社论:全脑仿真旨在通过系统识别实现思想及其一般智能
全脑模拟(WBE)是一种大规模神经假体的系统方法,目的是在其他操作基质中复制特定思维的功能。系统识别的工程实践可以应用于一种方式,使这个大问题成为一个可行的连接的小系统识别问题的集合来解决。全脑仿真是神经科学的一个重要目标。正如理查德·费曼1988年在加州理工学院黑板上的名言:“我不能创造的东西,我就不理解。”为了创造或构建人类思维,我们需要模型,即构建模块与过程的结合。当我们解释观察到的东西时,例如,心理功能和行为,我们努力使其在满足我们兴趣的约束下可预测:我们创建边界,我们在那些定义良好的轮廓内测量,然后我们使用这些测量来推导模型过程,从而实现结果预测。在我们模型的已定义的系统大纲中,考虑到已定义的信号集,我们用数学方法描述了相互作用(可以用信息论术语表示)。现代科学的每一个方面都依赖于创造事物的表征。在每种情况下,我们都关注我们感兴趣的信号和可观察到的(或行为)。然后,我们试着用函数来解释系统过程在做什么。在大脑功能方面,一些认知假肢工作,如南加州大学西奥多·w·伯杰(Theodore W. Berger)实验室的开创性努力,已经成功地实施了这些步骤,并产生了成功的实验结果(伯杰等人,2012)。伯杰的团队已经开发并测试了一种基于仿生芯片的实验性海马神经假体。一个传递函数被确定并用于复制大鼠海马区CA3生物神经回路的操作特性。在实验中,该假肢能够再现该区域的输入转化为该区域的输出的方式。这种开发神经假体的方法在大鼠身上取得了成功,目前正在灵长类动物身上进行测试(Marmarelis et al., 2013)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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