Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm

Nanoscale ◽  
2019 ◽  
Vol 11 (33) ◽  
pp. 15596-15604 ◽  
Author(s):  
Mohit Kumar ◽  
Sohail Abbas ◽  
Jung-Ho Lee ◽  
Joondong Kim
Keyword(s):  
Human Brain ◽  
Associative Learning ◽  
Resistive Switching ◽  
Learning Process ◽  
Learning Algorithm ◽  
Synaptic Response ◽  
Synaptic Functions ◽  
Pavlovian Learning

Synaptic response has been enhanced by 340 times by geometrical modulation of a ZnO-based memristor. The device showed a variety of comprehensive synaptic functions, including the Pavlovian associative learning process in the human brain.

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

scholarly journals KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

2020 ◽  
Author(s):  
Peng Zhang ◽  
Jianye Hao ◽  
Weixun Wang ◽  
Hongyao Tang ◽  
Yi Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prior Knowledge ◽  
Learning Process ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Policy Network ◽  
Human Knowledge ◽  
Learning Agents ◽  
The Common ◽  
Low Performance

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to finetune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on several control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.

scholarly journals Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

2020 ◽  
Author(s):  
Castro Mayleen Dorcas Bondoc ◽  
Tumibay Gilbert Malawit
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Video Tutorials ◽  
Learning Management ◽  
Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

Decision making for highway complex scenario by improved safety field with learning process

2021 ◽  
pp. 095440702110532
Author(s):  
Can Xu ◽  
Wanzhong Zhao ◽  
Jingqiang Liu ◽  
Feng Chen
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Action Sequence ◽  
Decision Algorithm ◽  
The Road

To improve the agility and efficiency of the highway decision-making system and overcome the local optimal dilemma of the existing safety field, this paper builds an improved safety field to reflect the advantage of the reachable states and the learning process is further employed to make the decision long-term optimal. Firstly, the improved safety field is prepared by the kinematic model-based prediction of surrounding vehicles and the boundary is determined elaborately to ensure real-time performance. Then, the field is constructed by three individual fields. One is the kinematic field, which is built based the safe-distance model to measure the colliding risk of both moving or no-moving objects accurately. Another is the road field that reflects the lane-marker constraint. The last is the efficiency field, which is introduced creatively to improve efficiency. Furthermore, the learning algorithm is adopted to learn the long-term optimal state-action sequence in the safety field. Finally, the simulations are conducted in Prescan platform to validate the feasibility of the improved safety field in complex scenarios. The results show that the proposed decision algorithm can always drive autonomous vehicle to the state with a long-term optimal payoff and can improve the overall performance compared to the existing pure safety field and the interaction-aware method.

Significant of Gradient Boosting Algorithm in Data Management System

2021 ◽  
Vol 9 (2) ◽  
pp. 85-100
Author(s):  
Md Saikat Hosen ◽  
Ruhul Amin
Keyword(s):  
Data Management ◽  
Text Classification ◽  
Learning Process ◽  
Learning Algorithm ◽  
Error Function ◽  
Data Management System ◽  
Gradient Boosting ◽  
Response Parameter ◽  
Boosting Algorithms ◽  
Boosting Algorithm

Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.

Simulation of Human Brain

2021 ◽  
pp. 150-160
Author(s):  
Navita Malik ◽  
Arun Solanki
Keyword(s):  
Human Brain ◽  
Computer Science ◽  
Learning Process ◽  
Intelligent System ◽  
Classroom Teaching ◽  
Reinforcement Material ◽  
Ict Tools ◽  
Mode Of Instruction ◽  
Teaching Learning

AI is a branch of computer science that gives the ability to a computer to think and make decisions like humans. It stimulates the human brain in the computer and makes appropriate decisions when required. AI-enabled education impacts the designing of curriculum, mode of instruction, and many more. The use of these tools revolutionizing the education sector with the progression of ICT tools have now become AI-enabled. The main feature of an AI-enabled tool is personalization. These AI-enabled tools work like intelligent assistants for the students. The intelligent system having features like answer the queries of the students, give assistance, support learning, provide or take assignments, and provide reinforcement material according to their opted courses. A teacher has a minimum intervention with this process and has the role of a facilitator only. This chapter concludes that the AI-enabled teaching-learning process can't replace the classroom teaching; instead, it is handy. In the future, AI could replace the need of a teacher in class to some extent.

scholarly journals Neural Dynamics of Associative Learning during Human Sleep

2019 ◽  
Vol 30 (3) ◽  
pp. 1708-1715
Author(s):  
Andrés Canales-Johnson ◽  
Emiliano Merlo ◽  
Tristan A Bekinschtein ◽  
Anat Arzi
Keyword(s):  
Associative Learning ◽  
Learning Process ◽  
Brain Activity ◽  
Theta Activity ◽  
Neural Dynamics ◽  
New Associations ◽  
Human Sleep ◽  
New Information ◽  
Early Acquisition ◽  
Dynamic Interplay

Abstract Recent evidence indicates that humans can learn entirely new information during sleep. To elucidate the neural dynamics underlying sleep-learning, we investigated brain activity during auditory–olfactory discriminatory associative learning in human sleep. We found that learning-related delta and sigma neural changes are involved in early acquisition stages, when new associations are being formed. In contrast, learning-related theta activity emerged in later stages of the learning process, after tone–odor associations were already established. These findings suggest that learning new associations during sleep is signaled by a dynamic interplay between slow-waves, sigma, and theta activity.

scholarly journals The role of associative learning process on the response of fledgling great tits (Parus major) to mobbing calls

2019 ◽  
Vol 22 (6) ◽  
pp. 1095-1103 ◽  
Author(s):  
Mylène Dutour ◽  
Jean-Paul Léna ◽  
Adeline Dumet ◽  
Vanessa Gardette ◽  
Nathalie Mondy ◽  
...  
Keyword(s):  
Associative Learning ◽  
Learning Process ◽  
Parus Major

scholarly journals Confidence Weighted Multitask Learning

2019 ◽  
Vol 33 ◽  
pp. 5636-5643
Author(s):  
Peng Yang ◽  
Peilin Zhao ◽  
Jiayu Zhou ◽  
Xin Gao
Keyword(s):  
Gaussian Distribution ◽  
Learning Process ◽  
Learning Algorithm ◽  
Multitask Learning ◽  
Local Component ◽  
Label Prediction ◽  
Learning Efficacy ◽  
Regret Bounds ◽  
The Mean ◽  
Theoretical Results

Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process. The mean (covariance) of the Gaussian Distribution is a sum of a local component and a global component that is shared among all the tasks. In addition, this paper also addresses the challenge of active learning on the online multitask setting. Instead of requiring labels of all the instances, the proposed algorithm determines whether the learner should acquire a label by considering the confidence from its related tasks over label prediction. Theoretical results show the regret bounds can be significantly reduced. Empirical results demonstrate that the proposed algorithm is able to achieve promising learning efficacy, while simultaneously minimizing the labeling cost.

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