scholarly journals An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 8789-8807 ◽  
Author(s):  
Ke Huang ◽  
Xin Ma ◽  
Rui Song ◽  
Xuewen Rong ◽  
Xincheng Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Architecture ◽  
Audiovisual Fusion

Single SNN Architecture for Classical and Operant Conditioning Using Reinforcement Learning

2020 ◽  
pp. 786-810
Author(s):  
Etienne Dumesnil ◽  
Philippe-Olivier Beaulieu ◽  
Mounir Boukadoum
Keyword(s):  
Neural Network ◽  
Operant Conditioning ◽  
Field Programmable Gate Array ◽  
Adaptive Systems ◽  
Negative Reinforcement ◽  
Cognitive Architecture ◽  
Spiking Neural Network ◽  
Low Resource ◽  
Field Programmable ◽  
The Stability

A bio-inspired robotic brain is presented where the same spiking neural network (SNN) can implement five variations of learning by conditioning (LC): classical conditioning (CC), and operant conditioning (OC) with positive/negative reinforcement/punishment. In all cases, the links between input stimuli, output actions, reinforcements and punishments are strengthened depending on the stability of the delays between them. To account for the parallel processing nature of neural networks, the SNN is implemented on a field-programmable gate array (FPGA), and the neural delays are extracted via an adaptation of the synapto-dendritic kernel adapting neuron (SKAN) model, for a low resource demanding FPGA implementation of the SNN. A custom robotic platform successfully tested the ability of the proposed architecture to implement the five LC behaviors. Hence, this work contributes to the engineering field by proposing a scalable low resource demanding architecture for adaptive systems, and the cognitive field by suggesting that both CC and OC can be modeled as a single cognitive architecture.

scholarly journals Artificial Neural Network Simulations of Human Learning Suggest the Presence of Metastable Attractors in Visual Memory

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Philippe Chassy ◽  
Frederic Surre
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Visual Memory ◽  
Human Performance ◽  
Cognitive Architecture ◽  
Stable State ◽  
Human Learning ◽  
Learning Curves ◽  
Long Term Memory

The attractor hypothesis states that knowledge is encoded as topologically-defined, stable configurations of connected cell assemblies. Irrespective to its original state, a network encoding new information will thus self-organize to reach the necessary stable state. To investigate memory structure, a multimodular neural network architecture, termed Magnitron, has been developed. Magnitron is a biologically-inspired cognitive architecture that simulates digit recognition. It implements perceptual input, human visual long-term memory in the ventral visual pathway and, to a lesser extent, working memory processes. To test the attractor hypothesis a Monte Carlo simulation of 10,000 individuals has been run. Each simulated learner was trained in recognizing the ten digits from novice to expert stage. The results replicate several features of human learning. First, they show that random connectivity in long-term visual memory accounts for novices’ performance. Second, the learning curves revealed that Magnitron simulates the well-known psychological power law of practice. Third, after learning took place, performance departed from chance level and reached a minimum target of 95% of correct hits; hence simulating human performance in children (i.e., when digits are learned). Magnitron also replicates biological findings. In line with research using voxel-based morphometry, Magnitron showed that matter density increases while training is taken place. Crucially, the spatial analysis of the connectivity patterns in long-term visual memory supported the hypothesis of a stable attractor. The significance of these results regarding memory theory is discussed.

A systematic approach for English education model based on the neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Wang Hui ◽  
Li Aiyuan
Keyword(s):  
Neural Network ◽  
Performance Improvement ◽  
Error Rate ◽  
English Education ◽  
Learning Ability ◽  
Fusion Method ◽  
Recognition Error ◽  
Parallel Data ◽  
Significant Performance ◽  
Audiovisual Fusion

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..

Scale-free architectures support representational diversity

2020 ◽  
Vol 43 ◽  
Author(s):  
Chris Fields ◽  
James F. Glazebrook
Keyword(s):  
Cognitive Architecture ◽  
Levels Of Abstraction ◽  
Scale Free ◽  
Abstract Representations ◽  
Computational Architecture ◽  
Architectural Models ◽  
Cognition Scale

Abstract Gilead et al. propose an ontology of abstract representations based on folk-psychological conceptions of cognitive architecture. There is, however, no evidence that the experience of cognition reveals the architecture of cognition. Scale-free architectural models propose that cognition has the same computational architecture from sub-cellular to whole-organism scales. This scale-free architecture supports representations with diverse functions and levels of abstraction.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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