An MOS cell circuit for compact implementation of reaction-diffusion models

Karahaliloglu, K.; Balkir, S.

doi:10.1109/ijcnn.2004.1379886

Cited by 5 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such reaction-diffusion networks are often constructed using CMOS technology [15]. Within the rectangular grid network, each node is a simplified version of a Hodgkin-Huxley neuron [1], described by the FitzHugh-Nagumo [2], [3] model, and each neuron is connected to all four nodes in its neighborhood with only diffusive coupling.…”

Section: Introductionmentioning

confidence: 99%

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

Dasmahapatra

Maharatna

2015

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

Dasmahapatra

Maharatna

2015

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…It was shown that in an excitable media state, the network can propagate voltage states as a trigger wave [4]. This excitation state can be altered if the bias current is chosen in the range around J P /2, where J P stands for the peak current from the cell in Fig.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…The cell capacitance C I can be an external capacitance or an approximation to the internal cell circuit capacitance. In particular, a cubic current response as a function of state variables v ij can provide bistable states in the phase space [4]. A compact cell circuit design capable of realizing such an I-V characteristic J ij (v ij ) is proposed and implemented.…”

Section: Implemented Cnn Layermentioning

confidence: 99%

“…Previously, a novel MOS cell design was presented which can be used to compactly implement the core dynamics in commonly observed reaction-diffusion mechanisms, especially the ones related to biological models [4]. It was shown that the single layer CNN formed by these cells was capable of propagating trigger waves.…”

Section: Introductionmentioning

confidence: 99%

“…The cell changes state toward stable states V L or V H , depending on these parameters[4]. Hence a bistable response is obtained.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Bio-Inspired CNN Layer with Image Processing Capabilities

Karahaliloglu

Balkir²,

Schemm³

2005 IEEE International Symposium on Circuits and Systems

Self Cite

View full text Add to dashboard Cite

Image processing capabilities of an implemented CNN layer are presented, where the network employs bioinspired MOS cells that exhibit cubic I-V characteristics. The network architecture and an approximate analysis of its certain D.C. characteristics are given. Certain image processing capabilities of the CNN layer are elicited by observing the inherent cooperative behavior among the cells when they are formed as a 2-D array. The fabricated CMOS test chip includes a 9 × 10 array of connected cells. Applications such as image smoothing, horizontal or vertical line detection, and characteristic noise tolerance have been demonstrated using the test chip. The cell circuit and the resulting network are suitable for large scale applications, since they are very compact and easily implementable.

show abstract

Bibliography

2005

Reaction-Diffusion Computers

View full text Add to dashboard Cite

An MOS cell circuit for compact implementation of reaction-diffusion models

Cited by 5 publications

References 13 publications

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

A Bio-Inspired CNN Layer with Image Processing Capabilities

Bibliography

Contact Info

Product

Resources

About