- 學位論文
整數二次項積分與激發神經模型
Integer Quadratic Integrate-and-Fire Neuron Model
摘要
進行突波神經網路之模擬時常需求解大量微分方程，而這使得現代電腦進行一實時處理因圖片而產生之光流的仿生視覺系統模擬時面臨一大挑戰。爲解決此一問題，我們開發了整數二次項積分與激發(Integer Quadratic Integrate-and-Fire, IQIF)模型，作爲新型的神經模型。IQIF 可產生與經典QIF 神經模型相同之激發模式，並將常見於模擬之浮點運算縮減爲整數運算；以受限制的膜電位與突觸電流動態範圍爲代價，此模型將提供對記憶體與邏輯閘較少需求之模擬方案。因此，IQIF 是一遵守生物可信性之模型，並可實作於邊緣運算平臺上，以達成低功耗、低成本之神經科學研究與機器學習應用。
並列摘要
Performing simulations of a spiking neural network evolves solving a large number of differential equations. This becomes a real challenge for the modern computer systems when one simulates a visual system that processes (optical flow)image signals in real time. To address the problem, we design a novel neuron model, the Integer Quadratic Integrate-and-Fire (IQIF) neuron. IQIF reproduces spiking behavior similar to the classical Quadratic Integrate-and-Fire (QIF) neuron but reduces all variables from commonly used floating points to integers, providing a simulation which requires significantly less memory and gates at the cost of limited dynamical ranges of membrane potential and synaptic current. IQIF is thus a bio-plausible-compliant model that can be applied to edge devices to achieve low-power low-cost neuroscience research and machine learning.