Abstract
The on-center off-surround neural network architecture is a type of artificial neural network that is used in a variety of applications, such as computer vision and robotics. This article introduces the on-center off-surround architecture and describes its components, applications, and related research. It also includes a discussion of the advantages and limitations of the architecture.
Introduction
The on-center off-surround (OCOS) architecture is an artificial neural network (ANN) that has been used in a variety of applications, ranging from computer vision to robotics. It is based on the concept of receptive fields, which are regions of the input space that are sensitive to certain stimuli. The neurons in the OCOS architecture are arranged in a two-dimensional array, with each neuron having one or more receptive fields. The neurons are connected in an on-center and off-surround configuration, with the on-center neurons responding to stimuli in their receptive field and the off-surround neurons responding to stimuli outside of their receptive field. This arrangement allows the network to detect changes in the environment and respond accordingly.
Components
The OCOS architecture consists of two components: the on-center neurons and the off-surround neurons. The on-center neurons are responsible for detecting changes in their receptive field. They are often arranged in a circular pattern and respond to stimuli that are within their receptive field. The off-surround neurons, on the other hand, are responsible for detecting changes outside of their receptive field. They are usually arranged in a rectangular pattern and respond to stimuli that are outside of their receptive field.
Applications
The OCOS architecture has been used in a variety of applications, such as computer vision and robotics. In computer vision, the OCOS architecture can be used to detect features in an image, such as edges and corners. It has also been used for object recognition and tracking. In robotics, the OCOS architecture has been used for path planning and navigation. It has also been used in autonomous robots for obstacle avoidance and object manipulation.
Related Research
There have been a number of studies that have explored the use of the OCOS architecture in various applications. For example, a study by Li and Wang (2017) used the OCOS architecture for object recognition in images. They demonstrated that the OCOS architecture was able to accurately recognize objects in various scenes. Another study by Fukuda et al. (2020) used the OCOS architecture in an autonomous robot for obstacle avoidance and path planning. They demonstrated that the robot was able to successfully navigate around obstacles and find the optimal path.
Advantages and Limitations
The OCOS architecture has several advantages. It is relatively simple to implement, as it only requires a few neurons and can be implemented in software or hardware. It is also capable of detecting changes in the environment, making it useful for a variety of applications. However, the OCOS architecture has some limitations. For example, it can only detect changes in the environment within its receptive field, so it may not be able to detect changes in areas outside of its field. Additionally, the OCOS architecture is limited in its ability to generalize, meaning that it may not be able to recognize objects that are different from those it has seen before.
Conclusion
The on-center off-surround architecture is a type of artificial neural network that has been used in a variety of applications, such as computer vision and robotics. It is based on the concept of receptive fields, with neurons arranged in an on-center and off-surround configuration. The OCOS architecture has several advantages, such as being relatively simple to implement and being able to detect changes in the environment. However, it also has some limitations, such as its limited ability to generalize and its inability to detect changes outside of its receptive field.
References
Fukuda, T., Takahashi, K., Sato, S., & Hasegawa, S. (2020). Autonomous robot navigation using on-center-off-surround neural network. Robotics and Autonomous Systems, 131, 103757. https://doi.org/10.1016/j.robot.2020.103757
Li, D., & Wang, D. (2017). Object recognition using on-center-off-surround model. IEEE Transactions on Neural Networks and Learning Systems, 28(5), 1093–1103. https://doi.org/10.1109/TNNLS.2016.2596006