{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T21:57:48Z","timestamp":1782424668931,"version":"3.54.5"},"reference-count":91,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as <jats:italic>machine-challenging tasks<\/jats:italic> (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: <jats:italic>Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances?<\/jats:italic> We hypothesized that a learning mechanism replicating the human brain is effective for tasks where machine intelligence is difficult. Multiple experiments corresponding to specific types of MCTs where machine intelligence has room to improve performance were performed using predictive coding, a more biologically plausible learning algorithm than backpropagation. This study regarded incremental learning, long-tailed, and few-shot recognition as representative MCTs. With extensive experiments, we examined the effectiveness of predictive coding that robustly outperformed backpropagation-trained networks for the MCTs. We demonstrated that predictive coding-based incremental learning alleviates the effect of catastrophic forgetting. Next, predictive coding-based learning mitigates the classification bias in long-tailed recognition. Finally, we verified that the network trained with predictive coding could correctly predict corresponding targets with few samples. We analyzed the experimental result by drawing analogies between the properties of predictive coding networks and those of the human brain and discussing the potential of predictive coding networks in general machine learning.<\/jats:p>","DOI":"10.3389\/fncom.2022.1062678","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T06:56:26Z","timestamp":1668581786000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks"],"prefix":"10.3389","volume":"16","author":[{"given":"Jangho","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeonghee","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byounghwa","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung-Hoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungroh","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"B1","first-page":"10913","article-title":"\u201cGait-prop: a biologically plausible learning rule derived from backpropagation of error,\u201d","author":"Ahmad","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33"},{"key":"B2","article-title":"\u201cDeep learning without weight transport,\u201d","author":"Akrout","year":"2019","journal-title":"Advances in Neural Information Processing Systems 32"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.pneurobio.2020.101821","article-title":"Prediction and memory: a predictive coding account","author":"Barron","year":"2020","journal-title":"Prog. Neurobiol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"E6798","DOI":"10.1073\/pnas.1510619112","article-title":"The modular and integrative functional architecture of the human brain","volume":"112","author":"Bertolero","year":"2015","journal-title":"Proc. Natl. Acad. Sci. U.S.A"},{"key":"B5","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1038\/nrn2335","article-title":"The hippocampus and memory: insights from spatial processing","volume":"9","author":"Bird","year":"2008","journal-title":"Nat. Rev. Neurosci"},{"key":"B6","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.jmp.2015.11.003","article-title":"A tutorial on the free-energy framework for modelling perception and learning","volume":"76","author":"Bogacz","year":"2017","journal-title":"J. Math. Psychol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jmp.2017.09.004","article-title":"The free energy principle for action and perception: a mathematical review","volume":"81","author":"Buckley","year":"2017","journal-title":"J. Math. Psychol"},{"key":"B8","article-title":"\u201cLearning imbalanced datasets with label-distribution-aware margin loss,\u201d","author":"Cao","year":"2019","journal-title":"Advances in Neural Information Processing Systems 32"},{"key":"B9","author":"Choksi","year":"2021","journal-title":"Advances in Neural Information Processing Systems 34"},{"key":"B10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/sj.npp.1301559","article-title":"Synaptic plasticity: multiple forms, functions, and mechanisms","volume":"33","author":"Citri","year":"2008","journal-title":"Neuropsychopharmacology"},{"key":"B11","doi-asserted-by":"publisher","first-page":"489","DOI":"10.31887\/DCNS.2010.12.4\/rcolom","article-title":"Human intelligence and brain networks","volume":"12","author":"Colom","year":"2022","journal-title":"Dialogues Clin. Neurosci"},{"key":"B12","doi-asserted-by":"publisher","first-page":"9268","DOI":"10.1109\/CVPR.2019.00949","article-title":"\u201cClass-balanced loss based on effective number of samples,\u201d","author":"Cui","year":"2019","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B13","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.tics.2014.12.004","article-title":"How the hippocampus preserves order: the role of prediction and context","volume":"19","author":"Davachi","year":"2015","journal-title":"Trends Cogn. Sci"},{"key":"B14","doi-asserted-by":"publisher","first-page":"025503","DOI":"10.1117\/1.JMI.6.2.025503","article-title":"Comparison of deep learning and human observer performance for detection and characterization of simulated lesions","volume":"6","author":"De Man","year":"2019","journal-title":"J. Med. Imaging"},{"key":"B15","first-page":"4937","article-title":"\u201cError-driven input modulation: solving the credit assignment problem without a backward pass,\u201d","author":"Dellaferrera","year":"2022","journal-title":"Proceedings of the 39th International Conference on Machine Learning"},{"key":"B16","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1016\/j.neuron.2017.05.016","article-title":"The brain as an efficient and robust adaptive learner","volume":"94","author":"Den\u00e9ve","year":"2017","journal-title":"Neuron"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv preprint arXiv:2010.11929"},{"key":"B18","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/nrn.2017.74","article-title":"Prefrontal-hippocampal interactions in episodic memory","volume":"18","author":"Eichenbaum","year":"2017","journal-title":"Nat. Rev. Neurosci"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/cercor\/1.1.1","article-title":"Distributed hierarchical processing in the primate cerebral cortex","volume":"1","author":"Felleman","year":"1991","journal-title":"Cereb. Cortex"},{"key":"B20","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","article-title":"Catastrophic forgetting in connectionist networks","volume":"3","author":"French","year":"1999","journal-title":"Trends Cogn. Sci"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.neunet.2003.06.005","article-title":"Learning and inference in the brain","volume":"16","author":"Friston","year":"2003","journal-title":"Neural Netw"},{"key":"B22","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1098\/rstb.2005.1622","article-title":"A theory of cortical responses","volume":"360","author":"Friston","year":"2005","journal-title":"Philos. Trans. R. Soc. B Biol. Sci"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000211","article-title":"Hierarchical models in the brain","author":"Friston","year":"2008","journal-title":"PLoS Comput. Biol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.06969","article-title":"Comparing deep neural networks against humans: object recognition when the signal gets weaker","author":"Geirhos","year":"2017","journal-title":"arXiv preprint arXiv:1706.06969"},{"key":"B25","article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","author":"Goodfellow","year":"2013","journal-title":"arXiv preprint arXiv:1312.6211"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6572","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv preprint arXiv:1412.6572"},{"key":"B27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1111\/j.1551-6708.1987.tb00862.x","article-title":"Competitive learning: from interactive activation to adaptive resonance","volume":"11","author":"Grossberg","year":"1987","journal-title":"Cogn. Sci"},{"key":"B28","article-title":"\u201cDeep predictive coding network with local recurrent processing for object recognition,\u201d","author":"Han","year":"2018","journal-title":"Advances in Neural Information Processing Systems 31"},{"key":"B29","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neuron.2017.06.011","article-title":"Neuroscience-inspired artificial intelligence","volume":"95","author":"Hassabis","year":"2017","journal-title":"Neuron"},{"key":"B30","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"\u201cDeep residual learning for image recognition,\u201d","author":"He","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B31","author":"Hebb","year":"2005","journal-title":"The Organization of Behavior: A Neuropsychological Theory"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-12016-9","article-title":"Hippocampal-neocortical interactions sharpen over time for predictive actions","volume":"10","author":"Hindy","year":"2019","journal-title":"Nat. Commun"},{"key":"B33","article-title":"\u201cLocal plasticity rules can learn deep representations using self-supervised contrastive predictions,\u201d","author":"Illing","year":"2021","journal-title":"Advances in Neural Information Processing Systems 34"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1607.00122","article-title":"Less-forgetting learning in deep neural networks","author":"Jung","year":"2016","journal-title":"arXiv preprint arXiv:1607.00122"},{"key":"B36","first-page":"14567","article-title":"\u201cDistribution aligning refinery of pseudo-label for imbalanced semi-supervised learning,\u201d","author":"Kim","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33"},{"key":"B37","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci. U.S.A"},{"key":"B38","author":"Krizhevsky","year":"2009","journal-title":"Learning Multiple Layers of Features From Tiny Images"},{"key":"B39","article-title":"\u201cImageNet classification with deep convolutional neural networks,\u201d","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems 25"},{"key":"B40","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"B41","article-title":"\u201cOne shot learning of simple visual concepts,\u201d","author":"Lake","year":"2011","journal-title":"Proceedings of the Annual Meeting of the Cognitive Science Society"},{"key":"B42","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"B43","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1007\/978-3-319-23528-8_31","article-title":"\u201cDifference target propagation,\u201d","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"Lee","year":"2015"},{"key":"B44","article-title":"\u201cOvercoming catastrophic forgetting by incremental moment matching,\u201d","volume-title":"Advances in Neural Information Processing Systems 30","author":"Lee","year":"2017"},{"key":"B45","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10279","article-title":"\u201cHow important is weight symmetry in backpropagation?\u201d","author":"Liao","year":"2016","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"B47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms13276","article-title":"Random synaptic feedback weights support error backpropagation for deep learning","volume":"7","author":"Lillicrap","year":"2016","journal-title":"Nat. Commun"},{"key":"B48","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/CVPR52688.2022.00019","article-title":"\u201cTowards better plasticity-stability trade-off in incremental learning: a simple linear connector,\u201d","author":"Lin","year":"2022","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B49","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/ICCV.2017.324","article-title":"\u201cFocal loss for dense object detection,\u201d","author":"Lin","year":"2017","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"B50","first-page":"21213","article-title":"\u201cLearning to learn with feedback and local plasticity,\u201d","author":"Lindsey","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-022-1375-7","article-title":"Denoised internal models: a brain-inspired autoencoder against adversarial attacks","author":"Liu","year":"2021","journal-title":"arXiv preprint arXiv:2111.10844"},{"key":"B52","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis","volume":"1","author":"Liu","year":"","journal-title":"Lancet Digit. Health"},{"key":"B53","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/CVPR.2019.00264","article-title":"\u201cLarge-scale long-tailed recognition in an open world,\u201d","author":"Liu","year":"","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.15277","article-title":"Class-incremental learning: survey and performance evaluation on image classification","author":"Masana","year":"2020","journal-title":"arXiv preprint arXiv:2010.15277"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.3389\/fncel.2019.00066","article-title":"The impact of studying brain plasticity","author":"Mateos-Aparicio","year":"2019","journal-title":"Front. Cell. Neurosci"},{"key":"B56","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","article-title":"\u201cCatastrophic interference in connectionist networks: the sequential learning problem,\u201d","author":"McCloskey","year":"1989","journal-title":"Psychology of Learning and Motivation"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00504","article-title":"The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects","author":"Mermillod","year":"2013","journal-title":"Front. Psychol"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2006.04182","article-title":"Predictive coding approximates backprop along arbitrary computation graphs","author":"Millidge","year":"2020","journal-title":"arXiv preprint arXiv:2006.04182"},{"key":"B59","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/nrn2303","article-title":"Synaptic plasticity, memory and the hippocampus: a neural network approach to causality","volume":"9","author":"Neves","year":"2008","journal-title":"Nat. Rev. Neurosci"},{"key":"B60","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.neuron.2012.03.024","article-title":"What makes a cell face selective? The importance of contrast","volume":"74","author":"Ohayon","year":"2012","journal-title":"Neuron"},{"key":"B61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-26022-3","article-title":"Neural heterogeneity promotes robust learning","volume":"12","author":"Perez-Nieves","year":"2021","journal-title":"Nat. Commun"},{"key":"B62","first-page":"7296","article-title":"\u201cKernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks,\u201d","author":"Pogodin","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1002\/wdev.216","article-title":"Neural plasticity across the lifespan","author":"Power","year":"2017","journal-title":"Wiley Interdiscipl. Rev. Dev. Biol"},{"key":"B64","doi-asserted-by":"publisher","first-page":"R764","DOI":"10.1016\/j.cub.2013.05.041","article-title":"Interplay of hippocampus and prefrontal cortex in memory","volume":"23","author":"Preston","year":"2013","journal-title":"Curr. Biol"},{"key":"B65","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1038\/4580","article-title":"Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects","volume":"2","author":"Rao","year":"1999","journal-title":"Nat. Neurosci"},{"key":"B66","first-page":"4175","article-title":"\u201cBalanced meta-softmax for long-tailed visual recognition,\u201d","author":"Ren","year":"2020","journal-title":"Advances in Neural Information Processing Systems 33"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0266102","article-title":"On the relationship between predictive coding and backpropagation","author":"Rosenbaum","year":"2021","journal-title":"arXiv preprint arXiv:2106.13082"},{"key":"B68","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"B69","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis"},{"key":"B70","article-title":"\u201cAssociative memories via predictive coding,\u201d","author":"Salvatori","year":"2021","journal-title":"Advances in Neural Information Processing Systems 34"},{"key":"B71","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/WACV48630.2021.00033","article-title":"\u201cFrom generalized zero-shot learning to long-tail with class descriptors,\u201d","author":"Samuel","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"B72","first-page":"4548","article-title":"\u201cOvercoming catastrophic forgetting with hard attention to the task,\u201d","volume-title":"International Conference on Machine Learning","author":"Serra","year":"2018"},{"key":"B73","article-title":"\u201cPrototypical networks for few-shot learning,\u201d","volume-title":"Advances in Neural Information Processing Systems 30","author":"Snell","year":"2017"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2110.05329","article-title":"Addressing the stability-plasticity dilemma via knowledge-aware continual learning","author":"Sokar","year":"2021","journal-title":"arXiv preprint arXiv:2110.05329"},{"key":"B75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-12306-2","article-title":"Stable memory with unstable synapses","volume":"10","author":"Susman","year":"2019","journal-title":"Nat. Commun"},{"key":"B76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep10253","volume":"5","author":"Suzuki","year":"2015"},{"key":"B77","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/B978-0-444-63327-9.00001-1","article-title":"Balancing plasticity\/stability across brain development","volume":"207","author":"Takesian","year":"2013","journal-title":"Prog. Brain Res"},{"key":"B78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-18325-8","article-title":"Rapid and dynamic processing of face pareidolia in the human brain","volume":"11","author":"Wardle","year":"2020","journal-title":"Nat. Commun"},{"key":"B79","first-page":"5266","article-title":"\u201cDeep predictive coding network for object recognition,\u201d","volume-title":"International Conference on Machine Learning","author":"Wen","year":"2018"},{"key":"B80","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1162\/NECO_a_00949","article-title":"An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity","volume":"29","author":"Whittington","year":"2017","journal-title":"Neural Comput"},{"key":"B81","article-title":"\u201cActivation sharing with asymmetric paths solves weight transport problem without bidirectional connection,\u201d","author":"Woo","year":"2021","journal-title":"Advances in Neural Information Processing Systems 34"},{"key":"B82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-27653-2","article-title":"Brain-inspired global-local learning incorporated with neuromorphic computing","volume":"13","author":"Wu","year":"2022","journal-title":"Nat. Commun"},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1708.07747","article-title":"Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv preprint arXiv:1708.07747"},{"key":"B84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-22244-7","article-title":"Limits to visual representational correspondence between convolutional neural networks and the human brain","volume":"12","author":"Xu","year":"2021","journal-title":"Nat. Commun"},{"key":"B85","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1038\/nature08577","article-title":"Stably maintained dendritic spines are associated with lifelong memories","volume":"462","author":"Yang","year":"2009","journal-title":"Nature"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.850945","article-title":"SAM: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory","author":"Yang","year":"","journal-title":"Front. Neurosci"},{"key":"B87","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.850932","article-title":"Heterogeneous ensemble-based spike-driven few-shot online learning","author":"Yang","year":"","journal-title":"Front. Neurosci"},{"key":"B88","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/e24040455","article-title":"Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion","volume":"24","author":"Yang","year":"","journal-title":"Entropy"},{"key":"B89","doi-asserted-by":"publisher","first-page":"13351","DOI":"10.1523\/JNEUROSCI.0607-15.2015","article-title":"Fast learning with weak synaptic plasticity","volume":"35","author":"Yger","year":"2015","journal-title":"J. Neurosci"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1710.09412","article-title":"mixup: beyond empirical risk minimization","author":"Zhang","year":"2017","journal-title":"arXiv preprint arXiv:1710.09412"},{"key":"B91","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-08931-6","article-title":"Humans can decipher adversarial images","volume":"10","author":"Zhou","year":"2019","journal-title":"Nat. Commun"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2022.1062678\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T06:56:41Z","timestamp":1668581801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2022.1062678\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":91,"alternative-id":["10.3389\/fncom.2022.1062678"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2022.1062678","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,16]]},"article-number":"1062678"}}