Segmentation, Binding, and Illusory Conjunctions

1991 ◽  
Vol 3 (4) ◽  
pp. 510-525 ◽  
Author(s):  
D. Horn ◽  
D. Sagi ◽  
M. Usher
Keyword(s):  
Neural Network ◽  
Human Vision ◽  
Inhibitory Neurons ◽  
Cell Assemblies ◽  
Inhibitory Cell ◽  
Excitatory Cell ◽  
Illusory Conjunctions

We investigate binding within the framework of a model of excitatory and inhibitory cell assemblies that form an oscillating neural network. Our model is composed of two such networks that are connected through their inhibitory neurons. The excitatory cell assemblies represent memory patterns. The latter have different meanings in the two networks, representing two different attributes of an object, such as shape and color. The networks segment an input that contains mixtures of such pairs into staggered oscillations of the relevant activities. Moreover, the phases of the oscillating activities representing the two attributes in each pair lock with each other to demonstrate binding. The system works very well for two inputs, but displays faulty correlations when the number of objects is larger than two. In other words, the network conjoins attributes of different objects, thus showing the phenomenon of “illusory conjunctions,” as in human vision.

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Control Method ◽  
Inhibitory Neurons ◽  
Robotic Arm ◽  
Control Approach ◽  
Planning And Control ◽  
Robotic Joints ◽  
And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

scholarly journals Advancing System Performance with Redundancy: From Biological to Artificial Designs

2019 ◽  
Vol 31 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Anh Tuan Nguyen ◽  
Jian Xu ◽  
Diu Khue Luu ◽  
Qi Zhao ◽  
Zhi Yang
Keyword(s):  
Neural Network ◽  
System Performance ◽  
Human Vision ◽  
The Other ◽  
Superior Performance ◽  
Fundamental Characteristic ◽  
Unified Theory ◽  
Biological Processes ◽  
Accuracy And Precision ◽  
Similar Operation

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended. Until recently, the only understanding of redundancy is as a mean to attain fault tolerance, which is reflected in the design of many man-made systems. On the contrary, our previous work on redundant sensing (RS) has demonstrated an example where redundancy can be engineered solely for enhancing accuracy and precision. The design was inspired by the binocular structure of human vision, which we believe may share a similar operation. In this letter, we present a unified theory describing how such utilization of redundancy is feasible through two complementary mechanisms: representational redundancy (RPR) and entangled redundancy (ETR). We also point out two additional examples where our new understanding of redundancy can be applied to justify a system's superior performance. One is the human musculoskeletal system (HMS), a biological instance, and the other is the deep residual neural network (ResNet), an artificial counterpart. We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems, as well as assist studies of the fundamental mechanisms governing various biological processes.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

scholarly journals Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition

2021 ◽  
Vol 12 (2) ◽  
pp. 93-110
Author(s):  
Garv Modwel ◽  
Anu Mehra ◽  
Nitin Rakesh ◽  
K. K. Mishra
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Human Vision ◽  
Biomedical Image Processing ◽  
X Ray ◽  
Biomedical Image ◽  
X Ray Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Result Analysis

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.

scholarly journals Image Classification in HTP Test Based on Convolutional Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lin Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Reliability And Validity ◽  
Feature Learning ◽  
Human Vision ◽  
Assessment Process ◽  
Learning Networks ◽  
Good Reliability

HTP test in psychometrics is a widely studied and applied psychological assessment technique. HTP test is a kind of projection test, which refers to the free expression of painting itself and its creativity. Therefore, the form of group psychological counselling is widely used in mental health education. Compared with traditional neural networks, deep learning networks have deeper and more network layers and can learn more complex processing functions. In this stage, image recognition technology can be used as an assistant of human vision. People can quickly get the information in the picture through retrieval. For example, you can take a picture of an object that is difficult to describe and quickly search the content related to it. Convolutional neural network, which is widely used in the image classification task of computer vision, can automatically complete feature learning on the data without manual feature extraction. Compared with the traditional test, the test can reflect the painting characteristics of different groups. After quantitative scoring, it has good reliability and validity. It has high application value in psychological evaluation, especially in the diagnosis of mental diseases. This paper focuses on the subjectivity of HTP evaluation. Convolutional neural network is a mature technology in deep learning. The traditional HTP assessment process relies on the experience of researchers to extract painting features and classification.

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