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Добірка наукової літератури з теми "Neuromorphic computer systems"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 липня 2025

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Статті в журналах з теми "Neuromorphic computer systems"

1

Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (2023): 9779. http://dx.doi.org/10.3390/s23249779.

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Анотація:

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructure

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2

Satnam Singh, Ishita Sabharwal, Shweta Kushwaha, Dr. Shilpi Jain, and Dr. Madhur Jain. "Enhancing Human-Machine Interaction: Leveraging Neuromorphic Chips for Adaptive Learning and Control in Neural Prosthetics and Artificial Intelligence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 933–40. http://dx.doi.org/10.32628/cseit241061135.

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This paper examines the integration of neuromorphic chips, AI, and neural prostheses to enhance human-machine interaction. Neuromorphic chips, modelled after the brain's neural architecture, enable efficient learning, adaptive behaviour, and energy-efficient processing in AI systems and prostheses. These chips improve pattern recognition, adaptive control, and integration with the human nervous system. In neural prostheses, they promise seamless brain-computer interfaces (BCI) to restore mobility for paralyzed individuals and enable precise control of devices for people with severe disabilitie

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3

Mikki, Said. "Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space." Symmetry 16, no. 4 (2024): 492. http://dx.doi.org/10.3390/sym16040492.

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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation o

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4

Sharma, Parul, Balwinder Raj, and Sandeep Singh Gill. "Spintronics Based Non-Volatile MRAM for Intelligent Systems." International Journal on Semantic Web and Information Systems 18, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijswis.310056.

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Анотація:

In this paper the spintronic-based memory MRAM is presented that showed how it can replace both SRAM and DRAM and provide the high speed with great chip size. Moreover, MRAM is the nonvolatile memory that provides great advancement in the storage process. The different types of MRAM are mentioned with the techniques used for writing purpose and also mention which one is more used and why. The basic working principle and the function performed by the MRAM are discussed. Artificial intelligence (AI) is mentioned with its pros and cons for intelligent systems. Neuromorphic computing is also expla

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5

Zhou, Jun. "Recent Progress of Memristor-based Neuromorphic Computing." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 1655–61. http://dx.doi.org/10.62051/1kany131.

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Анотація:

The evolution of memristors and their successful applications have positioned them as formidable candidates for the next generation of computer systems. With the rapid advancement of foundational ar- tificial intelligence applications, there is an increasing demand for computational power, energy efficiency, and stability. Memristors and the Neuromorphic Computing (NMC) systems they underpin hold signifi- can’t potential to break through the von Neumann bottleneck. However, technical challenges remain in the application of NMC to computer systems. In this review, we focus on the performance of

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6

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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Анотація:

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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7

Dunham, Christopher S., Sam Lilak, Joel Hochstetter, et al. "Nanoscale neuromorphic networks and criticality: a perspective." Journal of Physics: Complexity 2, no. 4 (2021): 042001. http://dx.doi.org/10.1088/2632-072x/ac3ad3.

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Abstract Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. The

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8

Siddique, Ali, Jingqi Sun, Kung Jui Hou, Mang I. Vai, Sio Hang Pun, and Muhammad Azhar Iqbal. "SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics." Agriculture 13, no. 11 (2023): 2057. http://dx.doi.org/10.3390/agriculture13112057.

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Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable smart aquaponic system requires estimating the fish size in real time. Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel h

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9

Jang, Taejin, Suhyeon Kim, Jeesoo Chang, et al. "3D AND-Type Stacked Array for Neuromorphic Systems." Micromachines 11, no. 9 (2020): 829. http://dx.doi.org/10.3390/mi11090829.

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NOR/AND flash memory was studied in neuromorphic systems to perform vector-by-matrix multiplication (VMM) by summing the current. Because the size of NOR/AND cells exceeds those of other memristor synaptic devices, we proposed a 3D AND-type stacked array to reduce the cell size. Through a tilted implantation method, the conformal sources and drains of each cell could be formed, with confirmation by a technology computer aided design (TCAD) simulation. In addition, the cell-to-cell variation due to the etch slope could be eliminated by controlling the deposition thickness of the cells. The sugg

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10

Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have alre

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Дисертації з теми "Neuromorphic computer systems"

1

Bieszczad, Andrzej Carleton University Dissertation Engineering Systems and Computer. "Neuromorphic distributed general problem solvers." Ottawa, 1996.

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2

Nease, Stephen Howard. "Contributions to neuromorphic and reconfigurable circuits and systems." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44923.

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Анотація:

This thesis presents a body of work in the field of reconfigurable and neuromorphic circuits and systems. Three main projects were undertaken. The first was using a Field-Programmable Analog Array (FPAA) to model the cable behavior of dendrites using analog circuits. The second was to design, lay out, and test part of a new FPAA, the RASP 2.9v. The final project was to use floating-gate programming to remove offsets in a neuromorphic FPAA, the RASP Neuron 1D.

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3

Azam, Md Ali. "Energy Efficient Spintronic Device for Neuromorphic Computation." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6036.

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Анотація:

Future computing will require significant development in new computing device paradigms. This is motivated by CMOS devices reaching their technological limits, the need for non-Von Neumann architectures as well as the energy constraints of wearable technologies and embedded processors. The first device proposal, an energy-efficient voltage-controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling. By controlling the domain wall motion utilizing spin transfer or spin orbit torques in association with voltage generated strain control

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4

Smith, Paul Devon. "An Analog Architecture for Auditory Feature Extraction and Recognition." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4839.

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Speech recognition systems have been implemented using a wide range of signal processing techniques including neuromorphic/biological inspired and Digital Signal Processing techniques. Neuromorphic/biologically inspired techniques, such as silicon cochlea models, are based on fairly simple yet highly parallel computation and/or computational units. While the area of digital signal processing (DSP) is based on block transforms and statistical or error minimization methods. Essential to each of these techniques is the first stage of extracting meaningful information from the speech signal, whi

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5

Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34755.

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Анотація:

Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонент

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6

Ramakrishnan, Shubha. "A system design approach to neuromorphic classifiers." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51718.

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Анотація:

This work considers alternative strategies to mainstream digital approaches to signal processing - namely analog and neuromorphic solutions, for increased computing efficiency. In the context of a speech recognizer application, we use low-power analog approaches for the signal conditioning and basic auditory feature extraction, while using a neuromorphic IC for building a dendritic classifier that can be used as a low-power word spotter. In doing so, this work also aspires to posit the significance of dendrites in neural computation.

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7

Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34756.

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Анотація:

Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонен

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8

Tully, Philip. "Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205568.

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Анотація:

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and cons

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9

Brink, Stephen Isaac. "Learning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50143.

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Анотація:

The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural co

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10

Bernard, Yann. "Calcul neuromorphique pour l'exploration et la catégorisation robuste d'environnement visuel et multimodal dans les systèmes embarqués." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0295.

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Tandis que la quête pour des systèmes de calcul toujours plus puissants se confronte à des contraintes matérielles de plus en plus fortes, des avancées majeures en termes d’efficacité de calcul sont supposées bénéficier d’approches non conventionnelles et de nouveaux modèles de calcul tels que le calcul inspiré du cerveau. Le cerveau est une architecture de calcul massivement parallèle avec des interconnexions denses entre les unités de calcul. Les systèmes neurobiologiques sont donc une source d'inspiration naturelle pour la science et l'ingénierie informatiques. Les améliorations technologiq

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Книги з теми "Neuromorphic computer systems"

1

1952-, Smith Leslie S., Hamilton Alister, and European Workshop on Neuromorphic Systems (1st : 1997 : University of Stirling), eds. Neuromorphic systems: Engineering silicon from neurobiology. World Scientific, 1998.

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2

1950-, Lande Tor Sverre, ed. Neuromorphic systems engineering: Neural networks in silicon. Kluwer Academic, 1998.

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3

Landolt, Oliver. Place Coding in Analog VLSI: A Neuromorphic Approach to Computation. Springer US, 1998.

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4

Liu, Shih-Chii, Giacomo Indiveri, Rodney Douglas, Tobi Delbruck, and Adrian Whatley. Event-Based Neuromorphic Systems. Wiley & Sons, Incorporated, John, 2015.

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5

Liu, Shih-Chii, Giacomo Indiveri, Rodney Douglas, Tobi Delbruck, and Adrian Whatley. Event-Based Neuromorphic Systems. Wiley & Sons, Incorporated, John, 2014.

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6

Liu, Shih-Chii, Giacomo Indiveri, Rodney Douglas, Tobi Delbruck, and Adrian Whatley. Event-Based Neuromorphic Systems. Wiley & Sons, Incorporated, John, 2014.

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7

Liu, Shih-Chii, Giacomo Indiveri, Rodney Douglas, Tobi Delbruck, and Adrian Whatley. Event-Based Neuromorphic Systems. Wiley & Sons, Limited, John, 2014.

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8

Liu, Shih-Chii, Giacomo Indiveri, Rodney Douglas, Tobi Delbruck, and Adrian Whatley. Event-Based Neuromorphic Systems. Wiley & Sons, Incorporated, John, 2015.

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9

Lande, Tor Sverre. Neuromorphic Systems Engineering: Neural Networks In Silicon. Springer, 2013.

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10

Liu, S. C. Neuromorphic and Bio-Inspired Engineered Systems. John Wiley and Sons Ltd, 2007.

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Частини книг з теми "Neuromorphic computer systems"

1

Haider, Muhammad Hamis, Hao Zhang, S. Deivalaskhmi, G. Lakshmi Narayanan, and Seok-Bum Ko. "Is Neuromorphic Computing the Key to Power-Efficient Neural Networks: A Survey." In Design and Applications of Emerging Computer Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-42478-6_4.

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2

Carstens, Niko, Maik-Ivo Terasa, Pia Holtz, et al. "Memristive Switching: From Individual Nanoparticles Towards Complex Nanoparticle Networks." In Springer Series on Bio- and Neurosystems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_9.

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AbstractNovel hardware concepts in the framework of neuromorphic engineering are intended to overcome fundamental limits of current computer technologies and to be capable of efficient mass data processing. To reach this, research into material systems which enable the implementation of memristive switching in electronic devices, as well as into analytical approaches helping to understand fundamental mechanisms and dynamics of memristive switching is inevitable. In this chapter, memristive switching based on Ag metal filament formation is discussed throughout different scales, providing insigh

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3

Carboni, Roberto. "Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications." In Special Topics in Information Technology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62476-7_5.

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AbstractWith the ubiquitous diffusion of mobile computing and Internet of Things (IoT), the amount of data exchanged and processed over the internet is increasing every day, demanding secure data communication/storage and new computing primitives. Although computing systems based on microelectronics steadily improved over the past 50 years thanks to the aggressive technological scaling, their improvement is now hindered by excessive power consumption and inherent performance limitation associated to the conventional computer architecture (von Neumann bottleneck). In this scenario, emerging mem

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4

Lewis, Rory, Michael Bihn, and Katrina Nesterenko. "Rough Sets for a Neuromorphic CMOS System." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62700-2_10.

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Fatima, Afroz, and Abhijit Pethe. "Embedded system with in-memory compute neuromorphic accelerator for multiple applications." In Smart Embedded Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781032628059-6.

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6

Liu, Ziyong, Xiaoxin Wang, Guiyao Xiang, Zhiyong Wang, Yitian Shao, and Honghai Liu. "A Neuromorphic Tactile Perception System Based on Spiking Neural Network for Texture Recognition." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0789-1_13.

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Huang, Lei, Pan Lv, Xin Du, Ouwen Jin, and Shuiguang Deng. "A Hierarchical Neural Task Scheduling Algorithm in the Operating System of Neuromorphic Computers." In Knowledge Science, Engineering and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5501-1_11.

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Malavena, Gerardo. "Modeling of GIDL–Assisted Erase in 3–D NAND Flash Memory Arrays and Its Employment in NOR Flash–Based Spiking Neural Networks." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_4.

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AbstractSince the very first introduction of three-dimensional (3–D) vertical-channel (VC) NAND Flash memory arrays, gate-induced drain leakage (GIDL) current has been suggested as a solution to increase the string channel potential to trigger the erase operation. Thanks to that erase scheme, the memory array can be built directly on the top of a $$n^+$$ n + plate, without requiring any p-doped region to contact the string channel and therefore allowing to simplify the manufacturing process and increase the array integration density. For those reasons, the understanding of the physical phenome

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Dey, Ajoy, Chetan Kadway, and Sounak Dey. "Towards On-Device Learning and Personalization: A Case of In-Car Driver Drowsiness Detection System Using Neuromorphic Computing." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87660-8_22.

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Antsiperov, Viacheslav. "Neuromorphic Image Coding Based on a Sample of Counts Partition by a System of Receptive Fields." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_33.

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Тези доповідей конференцій з теми "Neuromorphic computer systems"

1

Weng, Yijie, Yu Qi, Yueming Wang, and Gang Pan. "Neuromorphic model-based neural decoders for brain-computer interfaces: a comparative study." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798332.

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2

Mena Morales, Raphael, Pablo Miralles, Diviya Devani, et al. "Brainsat: Hardware Development of a Neuromorphic On-Board Computer Applied to Methane Detection from Low Earth Orbit." In IAF Space Systems Symposium, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078372-0121.

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3

Matinizadeh, Shadi, Arghavan Mohammadhassani, M. L. Varshika, Sarah Johari, Nagarajan Kandasamy, and Anup Das. "QUANTISENC++: A Fully-Configurable Many-Core Neuromorphic Hardware." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942719.

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4

Andrei, Vlad C., Alexandru P. Drǎgutoiu, Gabriel Béna, et al. "Deep- Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942794.

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5

Zendrikov, Dmitrii, Alessio Franci, and Giacomo Indiveri. "Waves and Symbols in Neuromorphic Hardware: From Analog Signal Processing to Digital Computing on the Same Computational Substrate." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10943060.

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6

Pei, Mengjiao, Ying Zhu, Siyao Liu, et al. "Ferroelectric Memcapacitor-based Reservoir Computing for High-efficiency Human-Computer Interface." In Neuromorphic Materials, Devices, Circuits and Systems. FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.025.

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Yousefzadeh, Amirreza, Gert-Jan van Schaik, Mohammad Tahghighi, et al. "SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture." In 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2022. http://dx.doi.org/10.1109/aicas54282.2022.9870025.

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8

Song, Chang, Beiye Liu, Chenchen Liu, Hai Li, and Yiran Chen. "Design techniques of eNVM-enabled neuromorphic computing systems." In 2016 IEEE 34th International Conference on Computer Design (ICCD). IEEE, 2016. http://dx.doi.org/10.1109/iccd.2016.7753356.

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Rajasekharan, Dinesh, Amit Ranjan Trivedi, and Yogesh Singh Chauhan. "Neuromorphic Circuits on FDSOI Technology for Computer Vision Applications." In 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). IEEE, 2019. http://dx.doi.org/10.1109/vlsid.2019.00108.

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Kutyniok, Gitta. "Reliable AI: From Legal Requirements to Neuromorphic Computing." In The 10th World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2024. http://dx.doi.org/10.11159/cist24.002.

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Звіти організацій з теми "Neuromorphic computer systems"

1

Gall, W. E. Brain-Based Devices for Neuromorphic Computer Systems. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587348.

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2

Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Анотація:

Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa

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