Demarse Thomas B, Wagenaar Daniel A, Blau Axel W, Potter Steve M
Division of Biology 156-29, California Institute of Technology, Pasadena, CA, 91125, USA.
Auton Robots. 2001;11(3):305-310. doi: 10.1023/a:1012407611130.
The brain is perhaps the most advanced and robust computation system known. We are creating a method to study how information is processed and encoded in living cultured neuronal networks by interfacing them to a computer-generated animal, the Neurally-Controlled Animat, within a virtual world. Cortical neurons from rats are dissociated and cultured on a surface containing a grid of electrodes (multi-electrode arrays, or MEAs) capable of both recording and stimulating neural activity. Distributed patterns of neural activity are used to control the behavior of the Animat in a simulated environment. The computer acts as its sensory system providing electrical feedback to the network about the Animat's movement within its environment. Changes in the Animat's behavior due to interaction with its surroundings are studied in concert with the biological processes (e.g., neural plasticity) that produced those changes, to understand how information is processed and encoded within a living neural network. Thus, we have created a hybrid real-time processing engine and control system that consists of living, electronic, and simulated components. Eventually this approach may be applied to controlling robotic devices, or lead to better real-time silicon-based information processing and control algorithms that are fault tolerant and can repair themselves.
大脑或许是已知的最先进、最强大的计算系统。我们正在创建一种方法,通过将培养的活体神经元网络与计算机生成的动物——神经控制动画机器人(Neurally-Controlled Animat)在虚拟世界中连接,来研究信息在其中是如何被处理和编码的。将大鼠的皮层神经元解离并培养在一个含有电极网格(多电极阵列,即MEA)的表面上,该电极网格能够记录和刺激神经活动。神经活动的分布式模式被用于在模拟环境中控制动画机器人的行为。计算机充当其感觉系统,向网络提供关于动画机器人在其环境中运动的电反馈。结合产生这些变化的生物学过程(如神经可塑性),研究动画机器人由于与周围环境相互作用而导致的行为变化,以了解信息在活体神经网络中是如何被处理和编码的。因此,我们创建了一个由活体、电子和模拟组件组成的混合实时处理引擎和控制系统。最终,这种方法可能会应用于控制机器人设备,或者带来更好的基于硅的实时信息处理和控制算法,这些算法具有容错能力且能够自我修复。