Ghaderi Viviane S, Roach Shane, Song Dong, Marmarelis Vasilis Z, Choma John, Berger Theodore W
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:767-70. doi: 10.1109/EMBC.2012.6346044.
The right level of abstraction for a model mimicking a neural function is often difficult to determine. There are trade-offs between capturing biological complexities on one hand and the scalability and efficiency of the model on the other. In this work, we describe a nonlinear Laguerre-Volterra model of the synaptic temporal integration of input spikes to postsynaptic potentials. This model is then efficiently implemented using analog subthreshold circuits and can serve as a foundation for future large-scale hardware systems that can emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error in estimating real data using the circuit implementation of this model is less than 15%. The model components are modular and its parameters are adjustable for modeling temporal integration by neurons in other brain regions. The total power consumption of this nonlinear Laguerre-Volterra system is less than 5nW.
对于模仿神经功能的模型而言，恰当的抽象层次通常很难确定。一方面要捕捉生物复杂性，另一方面要考虑模型的可扩展性和效率，这两者之间存在权衡。在这项工作中，我们描述了一个关于输入尖峰到突触后电位的突触时间整合的非线性拉盖尔 - 沃尔泰拉模型。然后使用模拟亚阈值电路有效地实现了该模型，它可以作为未来大规模硬件系统的基础，该系统能够模拟神经元群体中的多输入多输出（MIMO）尖峰变换。使用该模型的电路实现来估计实际数据时，归一化均方误差小于15%。模型组件是模块化的，其参数可调整，用于对其他脑区神经元的时间整合进行建模。这个非线性拉盖尔 - 沃尔泰拉系统的总功耗小于5纳瓦。
