Bio-inspired compact cell circuit for reaction-diffusion systems

Karahaliloglu, K.; Balkir, S.

doi:10.1109/tcsii.2005.850782

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…having at least one stable and active equilibrium point). On the opposite, wave-type dynamics is favored by the selection of parameter points in the UA (unstable and active) regions of the parameter space (5,6,7,9,10,13). It was also found that most interesting emergent behaviors occur taking cell parameter points from those regions (type 6 to 13) associated with a maximum number of equilibrium points (3 in our case).…”

Section: Locating Emergent Behaviors Genesmentioning

confidence: 54%

“…The advantage of using RD-CNNs for building image processors stands in their inherent parallelism and very simple coupling (resistive grids). Several analogue chips were already proposed in the literature [10] [11][12] [13], although we consider that from a practical point of view, digital models would be better. Speeding-up such models using GPU/CUDA approaches [14] are also convenient solutions in terms of good ratios between performances and costs.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

Dogaru

2013

2013 19th International Conference on Control Systems and Computer Science

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The possibility to exploit emergent computation in a naturally inspired complex network, namely the reactiondiffusion cellular nonlinear network (RD-CNN), is investigated. The particular application under focus is image processing. It is shown that by implementing a simplified discrete-time model and by using the local activity theory to locate potentially useful regions in the huge parameter space, many useful image processing tasks may be performed in reasonable execution time. Such tasks may include but are not limited to: feature extraction, image enhancement, noise removal, pattern formation, etc. A framework is provided for a systematic design allowing the identification of useful genes (sets of parameters) associated with meaningful image processing tasks.

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Section: Locating Emergent Behaviors Genesmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

Dogaru

2013

2013 19th International Conference on Control Systems and Computer Science

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“…Hence, the cell states are a bio-inspired system model is integrated with optical sensors excitable, which results in trigger wave propagation [12]. comprised of PN type photodiodes and the design is fabricated Implementation examples of FitzHugh-Nagumo models exist in CMOS technology.…”

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confidence: 99%

“…It was shown capacitance. In particular, a cubic (third order polynomial) numerically and verified through implementation, that the characteristic response as a function of state variable vij system model performed certain image processing tasks, such provides bistable states in the phase space [14], [12]. The as image smoothing, edge detection-enhancement.…”

mentioning

confidence: 99%

“…THE SYSTEM MODEL include artificial locomotion [4], [5] and path optimization The dynamical system model is a resistive-coupled 2D array [6]. A vast CNN application area regards image processing, with well-known and implemented examples such as [7]- [9]. of nonlinear cells which corresponds to a specific single layer withwelommownapproachorlelted systmpls dsign is to em-CNN as depicted in Fig.…”

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confidence: 99%

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Optical Sensor Integrated CNN for Real-Time Computational Applications

Karahaliloglu

Gans²,

Schemm³

et al.

2006 IEEE International Symposium on Circuits and Systems

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A previously implemented CNN layer based on Nagumo neuroelectric model [11]. Hence, the cell states are a bio-inspired system model is integrated with optical sensors excitable, which results in trigger wave propagation [12]. comprised of PN type photodiodes and the design is fabricated Implementation examples of FitzHugh-Nagumo models exist in CMOS technology. The implemented test chip is measured and obtained results are presented. The sensor integration marks a agan as OTA desilgns, such as in [13]. It was shown however, new step in prototyping of this system in CMOS, which allows that an essential part of similar functionality can be realized the cell states to be programmed via light interactions, rather using custom design approach [14], which will significantly than successive state programming procedure which was utilized improve the on-chip network size.previously. It is shown that the cell input states in the network In this aper we address a CMOS integration of our can be successfully introduced via exposure of the 2D array to p p g a light source. The followed custom design methods and this custom CNN design with PN type photodiodes. The result is remarkably compatible sensor integration can lead to compact a CNN compactly equipped with pixel sensors, which offers on-chip solutions for real-time high performance applications, great potential in computational applications as a stand-alone including but not limited to certain types of image processing. system or a building block. For this purpose, another test chip in 1.5 micron CMOS technology is implemented, which I. INTRODUCTION primarily validates the functionality of the sensor integrated The neuromorphic systems in nature are ultimate references design. Built upon this step, system adaptations to different for improving system models and their design methods. The applications will be rather straightforward, with peripheral cellular nonlinear (or neural) network (CNN) theory can modifications in terms of control circuitry which addresses extensively address such models. There are previous examples the particular computational task. Along with these efforts, of this parallelism in the literature, with their implementations. this sensor integrated CNN design will remarkably boost the A particular category deals with spatiotemporally excitable real time computational performance for specific applications. models, which are implementable via autonomous CNNs [1]-[3]. The frequently highlighted applications of such systems II. THE SYSTEM MODEL include artificial locomotion [4], [5] and path optimization The dynamical system model is a resistive-coupled 2D array [6]. A vast CNN application area regards image processing, with well-known and implemented examples such as [7]-[9]. of nonlinear cells which corresponds to a specific single layer withwelommownapproachorlelted systmpls dsign is to em-CNN as depicted in Fig. 1. The dynamical state equationThe common approach for relatedl systems dlesign iS to emotie rmtecretrlto o ahntoknd ''i ploy top-down oper...

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Unconventional Computing, Novel Hardware for

Asai¹

2009

Encyclopedia of Complexity and Systems Science

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Bio-inspired compact cell circuit for reaction-diffusion systems

Cited by 22 publications

References 22 publications

Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

Optical Sensor Integrated CNN for Real-Time Computational Applications

Unconventional Computing, Novel Hardware for

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