Neuroengineering and Bio-nanotechnology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy.
Comput Intell Neurosci. 2007;2007:12725. doi: 10.1155/2007/12725.
One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason "embodiment" represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.
智能行为的关键特性之一是能够学习和适应不断变化的环境条件。这些特征是大脑与外部世界不断强烈相互作用的结果，由身体介导。因此，“具体化”在研究学习新行为和适应意外情况的神经过程时，代表了一种创新且非常合适的实验范例。为此，我们开发了一种新型的双向神经接口。我们将从大鼠胚胎中提取并种植在微电极阵列（MEA）上的体外神经元相互连接到外部设备，从而实现实时闭环交互。这种实验方法的新颖之处在于需要探索不同的计算方案和实验假设。在本文中，我们提出了一种开放的、可扩展的架构，它允许快速原型设计不同的模块，并且可以测试编码和解码方案以及不同的实验配置。这个混合系统可用于研究生物神经网络的计算特性和信息编码，对未来先进神经假体的发展具有深远的意义。
