Dynamical System Guided Mapping of Quantitative Neuronal Models Onto Neuromorphic Hardware
Dynamical System Guided Mapping of Quantitative Neuronal Models Onto Neuromorphic Hardware
复制标题
DOI：
10.1109/tcsi.2012.2188956
复制
发表时间：
2012-10-01
影响因子：
5.1
通讯作者：
Boahen, Kwabena
中科院分区：
文献类型：
--
作者：
Gao, Peiran;Benjamin, Ben V.;Boahen, Kwabena
我们提出了一种利用动力系统理论的数学见解将神经元模型映射到神经形态硬件上的方法。定量准确的映射对于神经形态系统利用和扩展现有的理论和数值皮层建模结果非常重要。在本研究中，我们首先在定制硬件上校准片上偏置发生器。然后，利用硬件的高吞吐量尖峰通信，我们通过动态系统理论导出的静态输入的一组线性关系快速估计关键映射参数。我们将此映射过程应用于三个不同的芯片，并显示出与神经元模型以及芯片之间的紧密匹配 - Jenson-Shannon 散度减少到混洗控制的至少十分之一。我们确认我们的映射过程可以推广到动态输入：硅神经元与模拟神经元的尖峰时序相匹配，标准偏差为平均尖峰间间隔的 3.4%。
We present an approach to map neuronal models onto neuromorphic hardware using mathematical insights from dynamical system theory. Quantitatively accurate mappings are important for neuromorphic systems to both leverage and extend existing theoretical and numerical cortical modeling results. In the present study, we first calibrate the on-chip bias generators on our custom hardware. Then, taking advantage of the hardware's high-throughput spike communication, we rapidly estimate key mapping parameters with a set of linear relationships for static inputs derived from dynamical system theory. We apply this mapping procedure to three different chips, and show close matching to the neuronal model and between chips-the Jenson-Shannon divergence was reduced to at least one tenth that of the shuffled control. We confirm that our mapping procedure generalizes to dynamic inputs: Silicon neurons match spike timings of a simulated neuron with a standard deviation of 3.4% of the average inter-spike interval.