Institute of Informatics, University of Zurich and ETH Zurich, Zurich CH-8057, Switzerland.
Neural Comput. 2012 Jul;24(7):1669-94. doi: 10.1162/NECO_a_00293. Epub 2012 Mar 19.
Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.
神经科学家经常提出详细的计算模型来探究他们所研究的神经系统的特性。随着神经形态工程学的出现，越来越多的生物神经网络硬件电子模拟也被提出来。然而，对于生物和硬件系统来说，通常很难估计模型的参数，使其与所研究的实验系统具有意义，特别是当这些模型涉及到大量无法同时测量的状态和参数时。我们已经开发了一种使用最近开发的动态状态和参数估计（DSPE）技术来解决这个问题的方法。该技术使用同步作为一种工具，将实验测量的数据动态地耦合到其对应的模型中，以确定其参数和内部状态变量。通常，实验数据是从生物神经网络中获得的，模型是在软件中模拟的；在这里，我们表明该技术也可以有效地验证用于基于尖峰的神经形态超大规模集成（VLSI）芯片的网络模型，并且它能够系统地提取网络参数，如突触权重、时间常数和其他无法直接观察到的变量。我们的结果表明，这种方法可以成为基于模型的神经形态多芯片 VLSI 系统的识别和配置的非常有用的工具。
