Bio-Inspired Background Suppression Technique and its Implementation into Digital Circuit

Bio-Inspired Background Suppression Technique and its Implementation into Digital Circuit

Takao Yamanaka (Sophia University, Japan) and Yuta Munakata (Sophia University, Japan)
DOI: 10.4018/978-1-4666-2521-1.ch016


Gas sensors have been widely used for various applications, such as gas leak detection, fire alarm systems, and odor-sensing systems. A problem of the gas sensors has been the selectivity to a target gas: background gases interfere with the measurement of the target gas. In the human olfaction, sensitivity to background odors is decreased by adaptation to the odors. Recently, several bio-inspired signal-processing methods mimicking the adaptation mechanism have been proposed for improving the selectivity of the gas sensors. In this chapter, the studies on the bio-inspired background suppression methods are reviewed. Furthermore, a case study of the bio-inspired background suppression is introduced. In the case study, a perceptron neural network with anti-Hebbian learning was used for realizing the adaptation to the background gas, and was implemented into a digital circuit for real-time gas sensing.
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The adaptation is a common biological function in a sensory system, which allows the sensory system to reduce the sensitivity to previously detected stimuli, and then to improve the selectivity to a novel and interesting stimuli. In this chapter, we focus on the adaptation in the olfaction for developing bio-inspired signal processing methods for gas/odor sensing.

Neural computation models for olfactory adaptation or mixture segmentation have been proposed in various researchers (Figure 1). For example, Wang, Buhmann, and Malsburg (1990) proposed a neural model of pattern segmentation based on a neural network of associative memory, as shown in Figure 1 (a). They employed a neural network of oscillatory units linked with Hebbian connections to perform temporal segmentation of the stored patterns. Alternating bursts of activity induced by self-inhibition creates a spatio-temporal pattern that sequentially extracts the components of mixture.

Figure 1.

Neural computation models for olfactory adaptation or mixture segmentation. (a) Mutually connected oscillatory units (Wang, Buhmann, & Malsburg, 1990), (b) hierarchical layers of neural network (Hendin, Horn, & Hopfield, 1994), (c) hierarchical neural network model of olfactory cortex (Oyamada, Kashimori, Hoshino, & Kambara, 2000), (d) bulb-cortex model with feedforward and feedback paths (Li & Hertz, 2000), (e) KIII model (Yao & Freeman, 1990)

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