Academic literature on the topic 'Brain-inspired approaches'
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Journal articles on the topic "Brain-inspired approaches":
Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "Brain-Inspired Agents for Quantum Reinforcement Learning." Mathematics 12, no. 8 (April 19, 2024): 1230. http://dx.doi.org/10.3390/math12081230.
Ma, Gehua, He Wang, Jingyuan Zhao, Rui Yan, and Huajin Tang. "Successive POI Recommendation via Brain-Inspired Spatiotemporal Aware Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 574–82. http://dx.doi.org/10.1609/aaai.v38i1.27813.
Pham, Trung Quang, Teppei Matsui, and Junichi Chikazoe. "Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review." Biology 12, no. 10 (October 12, 2023): 1330. http://dx.doi.org/10.3390/biology12101330.
S Neves, Fabio, and Marc Timme. "Bio-inspired computing by nonlinear network dynamics—a brief introduction." Journal of Physics: Complexity 2, no. 4 (December 1, 2021): 045019. http://dx.doi.org/10.1088/2632-072x/ac3ad4.
Kułacz, Łukasz, and Adrian Kliks. "Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks." Journal of Telecommunications and Information Technology 1 (March 29, 2019): 39–46. http://dx.doi.org/10.26636/jtit.2019.130618.
Zheng, Tianyi, Wuhao Yang, Jie Sun, Zhenxi Liu, Kunfeng Wang, and Xudong Zou. "Processing IMU action recognition based on brain-inspired computing with microfabricated MEMS resonators." Neuromorphic Computing and Engineering 2, no. 2 (April 8, 2022): 024004. http://dx.doi.org/10.1088/2634-4386/ac5ddf.
Misra, Durgamadhab. "Special Issue of Interface on Neuromorphic Computing: An Introduction and State of the Field." Electrochemical Society Interface 32, no. 1 (March 1, 2023): 45–46. http://dx.doi.org/10.1149/2.f08231if.
Flor, Herta, Koichi Noguchi, Rolf-Detlef Treede, and Dennis C. Turk. "The role of evolving concepts and new technologies and approaches in advancing pain research, management, and education since the establishment of the International Association for the Study of Pain." Pain 164, no. 11S (November 2023): S16—S21. http://dx.doi.org/10.1097/j.pain.0000000000003063.
Kulhare, Rachna, and S. Veenadhari. "Feature Reduction in Classification Tasks using Bio-inspired Optimization Algorithms." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 04 (December 31, 2022): 72–78. http://dx.doi.org/10.18090/samriddhi.v14i04.12.
VASSILIADIS, VASSILIOS, and GEORGIOS DOUNIAS. "NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS." International Journal on Artificial Intelligence Tools 18, no. 04 (August 2009): 487–516. http://dx.doi.org/10.1142/s021821300900024x.
Dissertations / Theses on the topic "Brain-inspired approaches":
da, Silva Gomes Joao Paulo. "Brain inspired approach to computational face recognition." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3544.
Muliukov, Artem. "Étude croisée des cartes auto-organisatrices et des réseaux de neurones profonds pour l'apprentissage multimodal inspiré du cerveau." Electronic Thesis or Diss., Université Côte d'Azur, 2024. https://intranet-theses.unice.fr/2024COAZ4008.
Cortical plasticity is one of the main features that enable our capability to learn and adapt in our environment. Indeed, the cerebral cortex has the ability to self-organize itself through two distinct forms of plasticity: the structural plasticity and the synaptic plasticity. These mechanisms are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. The brain uses spatio-temporal correlations between several modalities to structure the data and create sense from observations. Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated. To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). Nevertheless, these frameworks do not provide a computational model at the neuron level, and only few works tackle this issue of bio-inspired multimodal association which is yet necessary for a complete representation of the environment especially when targeting autonomous and embedded intelligent systems. In this doctoral project, we propose to pursue the exploration of brain-inspired computational models of self-organization for multimodal unsupervised learning in neuromorphic systems. These neuromorphic architectures get their energy-efficient from the bio-inspired models they support, and for that reason we only consider in our work learning rules based on local and distributed processing
(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices." 2019.
Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks.
Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.
We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver.
Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.
Books on the topic "Brain-inspired approaches":
Lewin-Benham, Ann. Infants and toddlers at work: Using Reggio-inspired materials to support brain development. New York: Teachers College Press, 2010.
Lewin-Benham, Ann. Infants and toddlers at work: Using Reggio-inspired materials to support brain development. New York: Teachers College Press, 2010.
Cangelosi, Angelo, and Minoru Asada, eds. Cognitive Robotics. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13780.001.0001.
Paul, Sharon J. Art & Science in the Choral Rehearsal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190863760.001.0001.
Tousi, Babak. Cognitive Enhancement in Non-Alzheimer’s Dementias. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190214401.003.0004.
Temkin, Larry S. Being Good in a World of Need. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849977.001.0001.
Book chapters on the topic "Brain-inspired approaches":
Xie, Guoliang, Jinchang Ren, Huimin Zhao, Sophia Zhao, and Stephen Marshall. "Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis." In Advances in Brain Inspired Cognitive Systems, 342–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_33.
Hussien, Intisar O., Kia Dashtipour, and Amir Hussain. "Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect." In Advances in Brain Inspired Cognitive Systems, 615–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00563-4_60.
Minhas, Saliha, Soujanya Poria, Amir Hussain, and Khalid Hussainey. "A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure." In Advances in Brain Inspired Cognitive Systems, 317–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_36.
Deussen, Erik, Herwig Unger, and Mario M. Kubek. "Brain-Inspired Approaches to Natural Language Processing and Explainable Artificial Intelligence." In Innovations for Community Services, 6–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06668-9_2.
Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science, 105–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_8.
Pfeifer, Rolf. "Morphological Computation: Connecting Brain, Body, and Environment." In Biologically Inspired Approaches to Advanced Information Technology, 2–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11613022_2.
Abel, Andrew, Ricard Marxer, Jon Barker, Roger Watt, Bill Whitmer, Peter Derleth, and Amir Hussain. "A Data Driven Approach to Audiovisual Speech Mapping." In Advances in Brain Inspired Cognitive Systems, 331–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49685-6_30.
Chen, Zengqiang, Yuefei Wei, and Qinglin Sun. "An Improved Free Search Approach for Energy Optimization in Wireless Sensor Networks." In Advances in Brain Inspired Cognitive Systems, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_2.
Xiao, Yun, Fei Liu, Yabin Zhu, Chenglong Li, Futian Wang, and Jin Tang. "UAV Cross-Modal Image Registration: Large-Scale Dataset and Transformer-Based Approach." In Advances in Brain Inspired Cognitive Systems, 166–76. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1417-9_16.
Çakır, Murat Perit, Abdullah Murat Şenyiğit, Daryal Murat Akay, Hasan Ayaz, and Veysi İşler. "Evaluation of UAS Camera Operator Interfaces in a Simulated Task Environment: An Optical Brain Imaging Approach." In Advances in Brain Inspired Cognitive Systems, 62–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31561-9_7.
Conference papers on the topic "Brain-inspired approaches":
Romero, J. A., L. A. Diago, J. Shinoda, and I. Hagiwara. "Evaluation of Brain Models to Control a Robotic Origami Arm Using Holographic Neural Networks." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-48074.
Wang, Fangzhen, Rong Wu, and Yan Fang. "Nanomedicine photoluminescence crystal-inspired brain sensing approach." In Neural Imaging and Sensing 2018, edited by Qingming Luo and Jun Ding. SPIE, 2018. http://dx.doi.org/10.1117/12.2289891.
Zhang, Tielin, Yi Zeng, Dongcheng Zhao, and Bo Xu. "Brain-inspired Balanced Tuning for Spiking Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/229.
Bienstman, Peter, Joni Dambre, Andrew Katumba, Matthias Freiberger, Floris Laporte, and Alessio Lugnan. "Photonic reservoir computing: a brain-inspired approach for information processing." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/ofc.2018.m4f.4.
Zhang, Boyuan, Shuyuan Zhu, Tong Xie, Xibang Yang, Yahui Liu, and Bing Zeng. "Filamentary Convolution for Spoken Language Identification: A Brain-Inspired Approach." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446318.
H. Hamid, Oussama, and Jochen Braun. "Attractor Neural States: A Brain-Inspired Complementary Approach to Reinforcement Learning." In 9th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006580203850392.
Parsa, Maryam, Khaled N. Khasawneh, and Ihsen Alouani. "A Brain-inspired Approach for Malware Detection using Sub-semantic Hardware Features." In GLSVLSI '23: Great Lakes Symposium on VLSI 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583781.3590293.
Yang, Erfu, Amir Hussain, and Kevin Gurney. "A brain-inspired soft switching approach: towards a cognitive cruise control system." In International Conference on Control Engineering and Electronics Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/cceee140071.
Beiu, Valeriu, Basheer A. M. Madappuram, and Martin McGinnity. "On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -." In 2008 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). IEEE, 2008. http://dx.doi.org/10.1109/icsamos.2008.4664844.
Caversan, Fabio, Lucas Rodrigues, Geanderson Ferreira, Danilo De Almeida, Carlos Veber, Gabriel Dias Rezende Martins, Dr Nabih Jaber, and Dr George Pappas. "Towards a Neuro-Symbolic Approach to Bridge the Gap Between Brain and Mind-Inspired Models." In 6th European International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2023. http://dx.doi.org/10.46254/eu6.20230441.
Reports on the topic "Brain-inspired approaches":
Li, Xiao, GX Xu, FY Ling, ZH Yin, Y. Wei,, Y. Zhao, Xn Li, WC Qi, L. Zhao, and FR Liang. The dose-effect association between electroacupuncture sessions and its effect on chronic migraine: a protocol of a meta-regression of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0085.