Micromechanical Engineering of Connectivity in Living Neural Networks

为了促进生物信息处理理论的发展，拥有一种允许对神经网络中的连接进行先验规范的系统将是有用的。通过定义神经网络中的连接性，该系统将允许对任意神经元结构进行快速原型制作。理想情况下，这样的系统不仅能够概括体内结构，而且能够以用户定义的方式创建新的神经结构。这种用户定义的网络将允许对神经系统中的生物信息处理理论进行更严格的测试，并可能导致从总体上识别新的信息处理范例。在这个项目中，将解决以下问题：a)确定在外力作用下成功诱导神经突起的决定因素。b)将工程网络行为与网络行为模型进行比较，以评估神经元信息处理是线性的还是非线性的。c)开发允许形成多个连接的放大方法。

To facilitate the development of theories for biological information processing, it would be useful to have a system that permits a priori specification of the connection in a neural network. By defining the connectivity in a neural network, the system will allow rapid prototyping of arbitrary neuronal architectures. Ideally, such a system would be able to not only recapitulate in vivo structure, but also be able to create novel neural structures in a user-defined manner. Such user-defined networks will then allow more rigorous testing of theories for biological information processing in the nervous system and will potential result in the identification of novel information processing paradigms in general. In this project, the following issues will be addressed:a) Identify the determinants of successful neurite elicitation under applied force.b) Compare engineered network behavior to model network behavior to assess whether neuronal information processing is linear or nonlinear.c) Develop approaches to scale-up that allow multiple connections to be formed.