Toni Roberto, Spaletta Giulia, Casa Claudia Della, Ravera Simone, Sandri Giorgio
Department of Human Anatomy, University of Parma.
Acta Biomed. 2007;78 Suppl 1:67-83.
The development of neural networks and brain automata has made neuroscientists aware that the performance limits of these brain-like devices lies, at least in part, in their computational power. The computational basis of a. standard cybernetic design, in fact, refers to that of a discrete and finite state machine or Turing Machine (TM). In contrast, it has been suggested that a number of human cerebral activites, from feedback controls up to mental processes, rely on a mixing of both finitary, digital-like and infinitary, continuous-like procedures. Therefore, the central nervous system (CNS) of man would exploit a form of computation going beyond that of a TM. This "non conventional" computation has been called hybrid computation. Some basic structures for hybrid brain computation are believed to be the brain computational maps, in which both Turing-like (digital) computation and continuous (analog) forms of calculus might occur. The cerebral cortex and brain stem appears primary candidate for this processing. However, also neuroendocrine structures like the hypothalamus are believed to exhibit hybrid computional processes, and might give rise to computational maps. Current theories on neural activity, including wiring and volume transmission, neuronal group selection and dynamic evolving models of brain automata, bring fuel to the existence of natural hybrid computation, stressing a cooperation between discrete and continuous forms of communication in the CNS. In addition, the recent advent of neuromorphic chips, like those to restore activity in damaged retina and visual cortex, suggests that assumption of a discrete-continuum polarity in designing biocompatible neural circuitries is crucial for their ensuing performance. In these bionic structures, in fact, a correspondence exists between the original anatomical architecture and synthetic wiring of the chip, resulting in a correspondence between natural and cybernetic neural activity. Thus, chip "form" provides a continuum essential to chip "function". We conclude that it is reasonable to predict the existence of hybrid computational processes in the course of many human, brain integrating activities, urging development of cybernetic approaches based on this modelling for adequate reproduction of a variety of cerebral performances.
神经网络和脑自动机的发展使神经科学家意识到,这些类脑装置的性能限制至少部分在于它们的计算能力。事实上,标准控制论设计的计算基础指的是离散和有限状态机或图灵机(TM)的计算基础。相比之下,有人提出,从反馈控制到心理过程的许多人类大脑活动都依赖于有限的、类似数字的过程和无限的、类似连续的过程的混合。因此,人类的中枢神经系统(CNS)可能利用了一种超越图灵机的计算形式。这种“非传统”计算被称为混合计算。混合脑计算的一些基本结构被认为是脑计算图谱,其中可能会出现类似图灵(数字)的计算和连续(模拟)的微积分形式。大脑皮层和脑干似乎是这种处理的主要候选者。然而,像下丘脑这样的神经内分泌结构也被认为表现出混合计算过程,并可能产生计算图谱。当前关于神经活动的理论,包括布线和容积传递、神经元群选择以及脑自动机的动态演化模型,为自然混合计算的存在提供了依据,强调了中枢神经系统中离散和连续通信形式之间的合作。此外,最近出现的神经形态芯片,如用于恢复受损视网膜和视觉皮层活动的芯片,表明在设计生物相容性神经电路时假设离散 - 连续极性对于其后续性能至关重要。事实上,在这些仿生结构中,芯片的原始解剖结构与合成布线之间存在对应关系,从而导致自然和控制论神经活动之间的对应关系。因此,芯片“形式”为芯片“功能”提供了必不可少的连续性。我们得出结论,预测在许多人类大脑整合活动过程中存在混合计算过程是合理的,这促使基于这种建模开发控制论方法,以充分再现各种大脑性能。