Noise-Tolerant Analog Circuits for Sensory Segmentation Based on Symmetric STDP Learning

Abstract. We previously proposed a neural segmentation model suitable for implementation with complementary metal-oxide-semiconductor (CMOS) circuits. The model consists of neural oscillators mutually coupled through synaptic connections. The learning is governed by a symmetric spike-timing-dependent plasticity (STDP). Here we demonstrate and evaluate the circuit operation of the proposed model with a network consisting of six oscillators. Moreover, we explore the effects of mismatch in the threshold voltage of transistors, and demonstrate that the network was tolerant to mismatch (noise). Key words: Analog circuits, Neural networks, Spiking neurons, STDP, Neural segmentation, Noise tolerance

• Type: Conference
• Year: 2008
• Where: ICONIP
• Authors: Gessyca Maria Tovar, Tetsuya Asai, Yoshihito Amemiya
• Keywords: ICONIP 2008, Information Technology, Neural Oscillators, Neural Segmentation, Neural Segmentation Model