Abstract
The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (<100 Hz) with a capacitor of merely 6 fF, which is hosted in an FG metal-oxide-semiconductor field-effect transistor. The FG-LIF neuron also has the advantage of low operation power (<30 pW/spike). Finally, the proposed circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex.
Highlights
Ongoing research efforts into spiking neural networks (SNNs) attempt to gain a better understanding of the brain (Gerstner and Kistler, 2002; Markram, 2006) and/or realize its “electronic replicas” that partially imitate brain functionalities such as learning and memory (Mead, 1990; Jeong et al, 2013; Merolla et al, 2014; Qiao et al, 2015)
Recall that unlike capacitor-based integrators, the FG integrator in the proposed floating-gate-based leaky integrate-and-fire (FGLIF) neuron has the characteristic relaxation time defined by quantum mechanical tunneling dynamics through the tunnel barrier
In order to highlight the scalability of the proposed FGLIF neuron circuit, the FG integrator was compared with a switched-capacitor integrator comprising n metal-oxide-semiconductor field-effect transistor (MOSFET) switches (MR1–MRn) and one capacitor Cmem
Summary
Ongoing research efforts into spiking neural networks (SNNs) attempt to gain a better understanding of the brain (Gerstner and Kistler, 2002; Markram, 2006) and/or realize its “electronic replicas” that partially imitate brain functionalities such as learning and memory (Mead, 1990; Jeong et al, 2013; Merolla et al, 2014; Qiao et al, 2015) The former generally employs computational SNNs; a vast number of spiking neurons are simulated on computers in search of their behaviors relating to neuronal representation at both low and high levels (Markram, 2006).
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