Relevant bibliographies by topics / Brain-Like Artificial Intelligence

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Brain-Like Artificial Intelligence'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Brain-Like Artificial Intelligence"

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Tamukoh, Hakaru. "Brain-like Artificial Intelligence for RoboCup@Home Robots." Brain & Neural Networks 24, no. 4 (2017): 213–25. http://dx.doi.org/10.3902/jnns.24.213.

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Rosemarie, Velik. "BRAIN Journal - AI Reloaded: Objectives, Potentials, and Challenges of the Novel Field of Brain-Like Artificial Intelligence." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 3, no. 3 (2012): 25–54. https://doi.org/10.5281/zenodo.1043303.

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ABSTRACT The general objective of Artificial Intelligence (AI) is to make machines – particularly computers – do things that require intelligence when done by humans. In the last 60 years, AI has significantly progressed and today forms an important part of industry and technology. However, despite the many successes, fundamental questions concerning the creation of human-level intelligence in machines still remain open and will probably not be answerable when continuing on the current, mainly mathematic-algorithmically-guided path of AI. With the novel discipline of Brain-Like Artificial Inte
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Chen, Bowei. "Brain health: Disease correlation and artificial intelligence." Theoretical and Natural Science 47, no. 1 (2024): 77–81. http://dx.doi.org/10.54254/2753-8818/47/2024pj0115.

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The brain is one of the most vital organs for us to perform through daily activity. But due to the lifestyle changes within modern society as the consequences of advancement of technology, more health issues have got into the public. Dementia and stroke are the two leading disability causes that are highly associated with the changes in modern society and the lack of brain health concepts for the general population. This article focuses on the two diseases, and how does it impact on brain health and correlate with daily lifestyle, moreover, the aim is to prevent the occurrence and promote heal
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Yashchenko, V. O. "Artificial brain. Biological and artificial neural networks, advantages, disadvantages, and prospects for development." Mathematical machines and systems 2 (2023): 3–17. http://dx.doi.org/10.34121/1028-9763-2023-2-3-17.

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The article analyzes the problem of developing artificial neural networks within the framework of creating an artificial brain. The structure and functions of the biological brain are considered. The brain performs many functions such as controlling the organism, coordinating movements, processing information, memory, thinking, attention, and regulating emotional states, and consists of billions of neurons interconnected by a multitude of connections in a biological neural network. The structure and functions of biological neural networks are discussed, and their advantages and disadvantages a
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Rosemarie, Velik. "BRAIN Journal - Brain-Like Artificial Intelligence for Automation – Foundations, Concepts and Implementation Examples." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 4, no. 1-4 (2013): 26–54. https://doi.org/10.5281/zenodo.1043417.

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ABSTRACT Over the last decades, automation technology has made serious progress and can today automate a wide range of tasks having before needed human physical and mental abilities. Nevertheless, a number of important problem domains remain that cannot yet be handled by our current machines and computers. A few prominent examples are applications involving “realworld” perception, situation assessment, and decision-making tasks. Recently, researchers have suggested to use concepts of “Brain-Like Artificial Intelligence”, i.e. concepts inspired by the functioning principles of the human or anim
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Green, Leigh Ann. "Brain-like computing systems show neural functions in a single chip." Scilight 2023, no. 6 (2023): 061101. http://dx.doi.org/10.1063/10.0017230.

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K P, VISHNUPRIYA, JWALA JOSE, PRINCE JOY, SRITHA S, and GIBI K. S. "Brain-Inspired Artificial Intelligence: Revolutionizing Computing and Cognitive Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39825.

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Brain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will ana
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V, Yashchenko. "A new approach to the development of artificial intelligence similar to human intelligence." Artificial Intelligence 28, AI.2023.28(1)) (2023): 105–21. http://dx.doi.org/10.15407/jai2023.01.105.

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The article discusses a new approach to the creation of artificial neurons and neural networks as a means of developing artificial intelligence similar to natural. Based on the consideration and analysis of the works of physiologists on the structure and functions of a biological neuron, it was found that the information perceived by a person is stored in the neurons of the brain at the molecular level, it also suggested that the nucleus and endoplasmic reticulum are elements of processing, transformation and storage of temporary and long temporary memory. In addition, it was suggested that th
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Bindhu, N., and N. Mageswari. "Brain Controlled Car for Disabled Using Artificial Intelligence." Data Analytics and Artificial Intelligence 3, no. 2 (2023): 63–79. http://dx.doi.org/10.46632/daai/3/2/14.

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This paper considers the development of a brain driven car, which would be of great help to the physically disabled people. Since these cars will rely only on what the individual is thinking they will hence not require any physical movement on the part of the individual. The car integrates signals from a variety of sensors like video, weather monitor, anti-collision etc. it also has an automatic navigation system in case of emergency. The car works on the asynchronous mechanism of artificial intelligence. It’s a great advance of technology which will make the disabled, abled. In the 40s and 50
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Ekpar, Frank Edughom. "A Comprehensive Artificial Intelligence-Driven Healthcare System." European Journal of Electrical Engineering and Computer Science 8, no. 3 (2024): 1–6. http://dx.doi.org/10.24018/ejece.2024.8.3.617.

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The World Health Organization (WHO) states that millions of people worldwide suffer from severe health conditions like diabetes, cardiovascular diseases, stroke, autism, and epilepsy. Some of these conditions, like diabetes, have been on the rise in low-and middle-income countries (LMICs) recently. These conditions have a significant impact on mortality, disability, economic losses, and physical and emotional suffering. However, with more accurate diagnosis, early detection, and prediction of occurrence, these conditions can be treated and managed more effectively, and in some cases, even prev
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Dissertations / Theses on the topic "Brain-Like Artificial Intelligence"

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Salihoglu, Utku. "Toward a brain-like memory with recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210221.

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For the last twenty years, several assumptions have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural network should be coded in some way or another in one of the dynamical attractors of the brain, and retrieved by stimulating the network to trap its dynamics in the desired item’s basin of attraction. The second view shared by neural network researchers is to base
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Books on the topic "Brain-Like Artificial Intelligence"

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1937-, Aleksander Igor, ed. Neural computing architectures: The design of brain-like machines. North Oxford, 1989.

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International Conference on Neural Information Processing (4th 1997 New Zealand). Brain-like computing and intelligent information systems. Springer, 1997.

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David, Hutchison. Creating Brain-Like Intelligence: From Basic Principles to Complex Intelligent Systems. Springer Berlin Heidelberg, 2009.

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Edmonds, David, ed. Future Morality. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862086.001.0001.

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The world is changing so fast that it is hard to know how to think about what we ought to do. We barely have time to reflect on how scientific advances will affect our lives before they are upon us. New kinds of dilemma are springing up. Can robots be held responsible for their actions? Will artificial intelligence be able to predict criminal activity? Is the future gender-fluid? Should we strive to become post-human? Should we use drugs to improve our intimate relationships — or to reduce crime? Our intuitions about questions like these are often both weak and confused. This book presents pro
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Anderson, James A. The Past of the Future of Computation. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0001.

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Hand axes, language, and computers are tools that increase our ability to deal with the world. Computing is a cognitive tool and comes in several kinds: digital, analog, and brain-like. An analog telephone connects two telephones with a wire. Talking causes a current to flow on the wire. In a digital telephone the voltage is converted into groups of ones or zeros and sent at high speed from one telephone to the other. An analog telephone requires one simple step. A digital telephone requires several million discrete steps per second. Digital telephones work because the hardware has gotten much
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Book chapters on the topic "Brain-Like Artificial Intelligence"

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Rabaey, Jan. "Brain-like Cognitive Engineering Systems." In From Artificial Intelligence to Brain Intelligence. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-7.

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Özkural, Eray. "What Is It Like to Be a Brain Simulation?" In Artificial General Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35506-6_24.

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Mostufa, Shahriar, and Kisalaya Chakrabarti. "A Genre of Cognitive Evolutions Through Artificial Superintelligence and Robotics Technology." In Brain-like Super Intelligence from Bio-electromagnetism. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0232-9_4.

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Pramanik, Sudeshna, Jhimli Sarkar, Pushpendra Singh, Kanad Ray, and Anirban Bandyopadhyay. "Revisiting Self-Operating Mathematical Universe (SOMU) as a Theory for Artificial General Intelligence, AGI and G+ Consciousness." In Brain-like Super Intelligence from Bio-electromagnetism. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0232-9_6.

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Löbner, Sebastian, Thomas Gamerschlag, Tobias Kalenscher, Markus Schrenk, and Henk Zeevat. "Introduction." In Language, Cognition, and Mind. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_1.

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AbstractIn order to help to explain cognition, cognitive structures are assumed to be present in the mind/brain. While the empirical investigation of such structures is the task of cognitive psychology, the other cognitive science disciplines like linguistics, philosophy and artificial intelligence have an important role in suggesting hypotheses. Researchers in these disciplines increasingly test such hypotheses by empirical means themselves. In philosophy, the traditional way of referring to such structures is via concepts, i.e. those mental entities by which we conceive reality and with the
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Santillo, Silvia, Pierangelo Orlando, Luciano De Petrocellis, Luigia Cristino, Vittorio Guglielmotti, and Carlo Musio. "Molecular and Functional Diversity of Visual Pigments: Clues from the Photosensitive Opsin–Like Proteins of the Animal Model Hydra." In Brain, Vision, and Artificial Intelligence. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11565123_23.

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A. Markovics, Jen. "Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98343.

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There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deox
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Uden, Lorna, and Shijie Guan. "Neuroscience and Artificial Intelligence." In Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8686-0.ch009.

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AI can even outperform humans in many tasks such as winning games like Go and poker as well as engaging in creative endeavours in writing novels and music. Despite this, it is still a long way from building artificial human intelligence. Current AIs are only designed to excel in their intended functions and cannot generate knowledge to new tasks and situations. For AI to achieve artificial human intelligence requires us to study and understand the human brain. Neuroscience is the study of the anatomy and physiology of the human brain. It provides us interesting insights into how the brain work
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Talreja Wassan, Jyotsna, and Veena Ghuriani. "Recent Trends in Deep Learning for Conversational AI." In Artificial Intelligence. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.113250.

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Conversational AI has seen unprecedented growth in recent years due to which Chatbots have been made available. Conversational AI primarily focuses on text or speech inputs, identifying the intention behind them, and responding to users with relevant information. Natural Language Processing (NLP), Natural Language Understanding (NLU), Machine Learning (ML), and speech recognition offer a personalized experience that mimics human-like engagement in conversational AI systems. Conversational AI systems like Google Meena, Amazon’s Alexa, Facebook’s BlenderBot, and OpenAI’s GPT-3 are trained using
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M, Hugh. "Artificial neural networks." In Applications of Artificial Intelligence in Chemistry. Oxford University Press, 1993. http://dx.doi.org/10.1093/hesc/9780198557364.003.0002.

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This chapter covers artificial neural networks, which are an example of a connectionist model with many logical units that are connected in a network. It reviews the way in which the brain is believed to function, as the design of the artificial neural networks is inspired by the structure of that organ. It also explains that the neuron is the fundamental processing unit in a typical human brain. The chapter talks about the behaviour of the neuron as a threshold device that has a step-like shape known as a step function or Heaviside function. The chapter points out the crucial feature of the b
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Conference papers on the topic "Brain-Like Artificial Intelligence"

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Maier, Martin. "From Artificial Intelligence to Active Inference: “Natural Intelligence” – the Future of AI-Native 6G." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m1i.5.

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Today’s complex networks increasingly resemble biological superorganisms with brain-like cognitive capabilities. This paper elaborates on today’s AI and what it can become by using active inference to biomimic key elements found in living intelligent systems.
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Mijalkov, Mite, Oveis Jamialahmadi, Stefano Romeo, Giovanni Volpe, and Joana B. Pereira. "A novel method for quantifying men and women-like features in brain structure and function (Conference Presentation)." In Emerging Topics in Artificial Intelligence (ETAI) 2022, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2022. http://dx.doi.org/10.1117/12.2633276.

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Cedron, Francisco, Sara Alvarez-Gonzalez, Adrian Villegas-Duque, and Ana Ribas-Rodriguez. "Astrocytes in Artificial Intelligence: Boosting Neural Networks with Biologically Inspired Models." In VII Congreso XoveTIC: impulsando el talento científico. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.62.

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Since its discovery in neuroscience, the tripartite synapse has inspired new architectures in the already ubiquitous artificial neural networks. This paper explains the frameworks proposed in artificial intelligence derived from the behaviour of the biological brain incorporating the called artificial astrocytes or artificial glia. First, we analyze the first structures on shallow networks, to later discuss the more convoluted configurations used in the past few years. We can classify them based on their degree of brain-like appearance, for some authors modeled them with this idea in mind (usi
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Cao, Lu, Dandan Huang, and Yue Zhang. "When Computational Representation Meets Neuroscience: A Survey on Brain Encoding and Decoding." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/594.

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Real human language mechanisms and the artificial intelligent language processing methods are two independent systems. Exploring the relationship between the two can help develop human-like language models and is also beneficial to reveal the neuroscience of the reading brain. The flourishing research in this interdisciplinal research field calls for surveys to systemically study and analyze the recent successes. However, such a comprehensive review still cannot be found, which motivates our work. This article first briefly introduces the interdisciplinal research progress, then systematically
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Ni, Yang, Yeseong Kim, Tajana Rosing, and Mohsen Imani. "Algorithm-Hardware Co-Design for Efficient Brain-Inspired Hyperdimensional Learning on Edge (Extended Abstract)." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/723.

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In this paper, we propose an efficient framework to accelerate a lightweight brain-inspired learning solution, hyperdimensional computing (HDC), on existing edge systems. Through algorithm-hardware co-design, we optimize the HDC models to run them on the low-power host CPU and machine learning accelerators like Edge TPU. By treating the lightweight HDC learning model as a hyper-wide neural network, we exploit the capabilities of the accelerator and machine learning platform, while reducing training runtime costs by using bootstrap aggregating. Our experimental results conducted on mobile CPU a
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Moreira da Silva, Ana. "Sketches versus Artificial Intelligence Systems in Design Creative Process." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002866.

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Artificial Intelligence Systems are part of our daily life and play an important role in so many human activities. As most professions, design practice also relies on artificial intelligence tools. Creativity is an essential element for design as designer’s task is searching to solve problems by creating solutions through new ideas, new designs. Sketching has been traditionally used by designers over the centuries, so it is often seen as an outdated method for design, as tradition can be thought as a strong and unalterable link to the past. In this sense, some consider sketching obsolete, but
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Déli, Eva K., and Zoltan Kiss. "THE PHYSICS OF INTELLIGENCE: CAN THE FERMIONIC MIND HYPOTHESIS PROVIDE A THEORETICAL FRAMEWORK FOR UNDERSTANDING CONSCIOUSNESS?" In 10th International Congres of the Serbian Society of Mechanics. Serbian Society of Mechanics, Belgrade, 2025. https://doi.org/10.46793/icssm25.341d.

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The rapidly evolving field of artificial intelligence has sparked considerable discussion regarding the potential for artificial consciousness. However, achieving this goal relies on a precise understanding of biological consciousness. At its core, consciousness operates similarly to large language models, functioning as an endothermic information processor and a generator of intellect. Perception arises from a reversible, probabilistic thermodynamic cycle, which imparts probabilistic and quantum characteristics to cognition. At the same time, consciousness possesses intrinsic unity and a fund
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Lee, Soo-Young. "Artificial Brain based on Brain-Inspired Algorithms for Human-Like Intelligent Functions." In 2007 IEEE International Conference on Integration Technology. IEEE, 2007. http://dx.doi.org/10.1109/icitechnology.2007.4290357.

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Ali, Haider abdullah, Nicolae Goga, Constantin viorel Marian, and Liwa Abdullah ali. "AN INVESTIGATION OF MIND-CONTROLLED PROSTHETIC ARM INTELLIGENT SYSTEM." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-088.

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Brain-Computer Interface (BCI) is a connection path that directly connects the system of central nervous and external devices without the reliance of peripheral nerves. The applications of brain-computer interface attain beyond medical applications where it used to improve the life of patients. Many achievements were established during the latest years in the prosthetics field particularly using the latest achievements of technology into these products. However, some problems have emerged in the prosthetics like being cost-expensive, difficult to install and maintain, or the need for surgical
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Fagiani, Ramona, Francesco Massi, Eric Chatelet, and Yves Berthier. "Dynamic Analysis of Surface Scanning for Tactile Perception." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25246.

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The human hand works in a perfect accord with the brain for an efficient exploration of physical world and objects perception according to man’s purposes. During the haptic sensing, the fingertip slides on a surface activating the receptors located under the skin allowing the brain to identify objects and information about their properties. In fact, in order to create the contact, the hand must exercise a force causing the fingertip to deform, generating a stress-state that contains the information on the object in contact. The information concerning the object surface is represented by the vi
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