{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:29:02Z","timestamp":1772501342663,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korea Government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korea Government (MSIT)","doi-asserted-by":"publisher","award":["IITP-2023-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2023-RS-2022-00156225"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science and ICT (MSIT)","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}]},{"name":"Ministry of Science and ICT (MSIT)","award":["IITP-2023-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2023-RS-2022-00156225"]}]},{"name":"Kwangwoon University","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}]},{"name":"Kwangwoon University","award":["IITP-2023-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2023-RS-2022-00156225"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models.<\/jats:p>","DOI":"10.3390\/s23167232","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:47:02Z","timestamp":1692269222000},"page":"7232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0726-1628","authenticated-orcid":false,"given":"Geunbo","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"given":"Wongyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"given":"Youjung","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"given":"Choongseop","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8214-1397","authenticated-orcid":false,"given":"Woojoon","family":"Seok","sequence":"additional","affiliation":[{"name":"Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"given":"Jongkil","family":"Park","sequence":"additional","affiliation":[{"name":"Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-9932","authenticated-orcid":false,"given":"Donggyu","family":"Sim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8042-007X","authenticated-orcid":false,"given":"Cheolsoo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","article-title":"Networks of spiking neurons: The third generation of neural network models","volume":"10","author":"Maass","year":"1997","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"291","DOI":"10.5573\/IEIESPC.2021.10.4.291","article-title":"Image Denoising Method based on Deep Learning using Improved U-net","volume":"10","author":"Han","year":"2021","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s13534-021-00185-w","article-title":"Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions","volume":"11","author":"Moni","year":"2021","journal-title":"Biomed. Eng. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"O\u2019reilly, R.C., and Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press.","DOI":"10.7551\/mitpress\/2014.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3389\/fninf.2018.00079","article-title":"Recurrent spiking neural network learning based on a competitive maximization of neuronal activity","volume":"12","author":"Demin","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","article-title":"A quantitative description of membrane current and its application to conduction and excitation in nerve","volume":"117","author":"Hodgkin","year":"1952","journal-title":"J. Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","article-title":"Simple model of spiking neurons","volume":"14","author":"Izhikevich","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00422-006-0068-6","article-title":"A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input","volume":"95","author":"Burkitt","year":"2006","journal-title":"Biol. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1523\/JNEUROSCI.02-01-00032.1982","article-title":"Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex","volume":"2","author":"Bienenstock","year":"1982","journal-title":"J. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1162\/089976603321891783","article-title":"Relating stdp to bcm","volume":"15","author":"Izhikevich","year":"2003","journal-title":"Neural Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4693","DOI":"10.1007\/s11063-021-10562-2","article-title":"A survey of encoding techniques for signal processing in spiking neural networks","volume":"53","author":"Auge","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.procs.2018.01.075","article-title":"Solving a classification task by spiking neurons with STDP and temporal coding","volume":"123","author":"Sboev","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Merolla, P., Arthur, J., Akopyan, F., Imam, N., Manohar, R., and Modha, D.S. (2011, January 19\u201321). A digital neurosynaptic core using embedded crossbar memory with 45 pJ per spike in 45 nm. Proceedings of the 2011 IEEE Custom Integrated Circuits Conference (CICC), San Jose, CA, USA.","DOI":"10.1109\/CICC.2011.6055294"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hussain, S., Liu, S.C., and Basu, A. (2014, January 1\u20135). Improved margin multi-class classification using dendritic neurons with morphological learning. Proceedings of the 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia.","DOI":"10.1109\/ISCAS.2014.6865715"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1109\/TVLSI.2013.2294916","article-title":"Minitaur, an event-driven FPGA-based spiking network accelerator","volume":"22","author":"Neil","year":"2014","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"332","DOI":"10.5573\/IEIESPC.2022.11.5.332","article-title":"A Painting Style System using an Improved CNN Algorithm","volume":"11","author":"Zhong","year":"2022","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tavanaei, A., Kirby, Z., and Maida, A.S. (2018, January 8\u201313). Training spiking convnets by stdp and gradient descent. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489104"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"682","DOI":"10.3389\/fnins.2017.00682","article-title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification","volume":"11","author":"Rueckauer","year":"2017","journal-title":"Front. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1162\/neco.2007.19.11.2881","article-title":"Learning real-world stimuli in a neural network with spike-driven synaptic dynamics","volume":"19","author":"Brader","year":"2007","journal-title":"Neural Comput."},{"key":"ref_21","unstructured":"Bohte, S.M., Kok, J.N., and La Poutr\u00e9, J.A. (2000, January 26\u201328). SpikeProp: Backpropagation for networks of spiking neurons. Proceedings of the ESANN, Bruges, Belgium."},{"key":"ref_22","unstructured":"Sacramento, J., Ponte Costa, R., Bengio, Y., and Senn, W. (2018, January 3\u20138). Dendritic cortical microcircuits approximate the backpropagation algorithm. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jphysparis.2006.10.001","article-title":"A free energy principle for the brain","volume":"100","author":"Friston","year":"2006","journal-title":"J. Physiol.-Paris"},{"key":"ref_24","doi-asserted-by":"crossref","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":"ref_25","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1038\/s41467-019-11786-6","article-title":"A critique of pure learning and what artificial neural networks can learn from animal brains","volume":"10","author":"Zador","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1073\/pnas.1820458116","article-title":"Unsupervised learning by competing hidden units","volume":"116","author":"Krotov","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Khacef, L., Rodriguez, L., and Miramond, B. (2020). Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning. Electronics, 9.","DOI":"10.3390\/electronics9101605"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/72.846729","article-title":"Self organization of a massive document collection","volume":"11","author":"Kohonen","year":"2000","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.3389\/fnins.2020.615756","article-title":"A heterogeneous spiking neural network for unsupervised learning of spatiotemporal patterns","volume":"14","author":"She","year":"2021","journal-title":"Front. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hebb, D.O. (2005). The Organization of Behavior: A Neuropsychological Theory, Psychology Press.","DOI":"10.4324\/9781410612403"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1093\/brain\/awl082","article-title":"Plasticity in the human central nervous system","volume":"129","author":"Cooke","year":"2006","journal-title":"Brain"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1146\/annurev-neuro-070815-013851","article-title":"Correlations and neuronal population information","volume":"39","author":"Kohn","year":"2016","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0959-4388(03)00034-5","article-title":"Neural population codes","volume":"13","author":"Sanger","year":"2003","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3389\/fncom.2015.00099","article-title":"Unsupervised learning of digit recognition using spike-timing-dependent plasticity","volume":"9","author":"Diehl","year":"2015","journal-title":"Front. Comput. Neurosci."},{"key":"ref_35","unstructured":"LeCun, Y. (2018, October 19). The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A. (2017, January 14\u201319). EMNIST: Extending MNIST to handwritten letters. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref_37","unstructured":"Zhang, J. (2019). Basic neural units of the brain: Neurons, synapses and action potential. arXiv."},{"key":"ref_38","unstructured":"Deneve, S. (2004, January 13\u201318). Bayesian inference in spiking neurons. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.tics.2006.05.003","article-title":"Bayesian decision theory in sensorimotor control","volume":"10","author":"Wolpert","year":"2006","journal-title":"Trends Cogn. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1126\/science.1142998","article-title":"Decision theory: What \u201cshould\u201d the nervous system do?","volume":"318","author":"Kording","year":"2007","journal-title":"Science"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s00422-006-0133-1","article-title":"Bayesian processing of vestibular information","volume":"96","author":"Laurens","year":"2007","journal-title":"Biol. Cybern."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Stevenson, I.H., Fernandes, H.L., Vilares, I., Wei, K., and K\u00f6rding, K.P. (2009). Bayesian integration and non-linear feedback control in a full-body motor task. PLoS Comput. Biol., 5.","DOI":"10.1371\/journal.pcbi.1000629"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1038\/nn.3495","article-title":"Probabilistic brains: Knowns and unknowns","volume":"16","author":"Pouget","year":"2013","journal-title":"Nat. Neurosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3389\/fninf.2018.00089","article-title":"Bindsnet: A machine learning-oriented spiking neural networks library in python","volume":"12","author":"Hazan","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gerstner, W., and Kistler, W.M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge University Press.","DOI":"10.1017\/CBO9780511815706"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","article-title":"Deep learning in spiking neural networks","volume":"111","author":"Tavanaei","year":"2019","journal-title":"Neural Netw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104828","DOI":"10.1016\/j.nanoen.2020.104828","article-title":"Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks","volume":"74","author":"Yang","year":"2020","journal-title":"Nano Energy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1109\/TED.2017.2671353","article-title":"Proposal for a leaky-integrate-fire spiking neuron based on magnetoelectric switching of ferromagnets","volume":"64","author":"Jaiswal","year":"2017","journal-title":"IEEE Trans. Electron. Devices"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MC.2004.1297236","article-title":"Computational challenges of systems biology","volume":"37","author":"Finkelstein","year":"2004","journal-title":"Computer"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gerstner, W., Kistler, W.M., Naud, R., and Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, Cambridge University Press.","DOI":"10.1017\/CBO9781107447615"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"10464","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998","article-title":"Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type","volume":"18","author":"Bi","year":"1998","journal-title":"J. Neurosci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neunet.2019.08.016","article-title":"Locally connected spiking neural networks for unsupervised feature learning","volume":"119","author":"Saunders","year":"2019","journal-title":"Neural Netw."},{"key":"ref_53","unstructured":"Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D., and Darnell, J. (2000). Molecular Cell Biology, WH Freeman. [4th ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1093\/cercor\/bhy225","article-title":"Inhibitory neuron activity contributions to hemodynamic responses and metabolic load examined using an inhibitory optogenetic mouse model","volume":"28","author":"Vazquez","year":"2018","journal-title":"Cereb. Cortex"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1152\/jn.00447.2017","article-title":"Intrinsic physiology of inhibitory neurons changes over auditory development","volume":"119","author":"Carroll","year":"2018","journal-title":"J. Neurophysiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3389\/fnins.2012.00165","article-title":"Inhibitory control of hippocampal inhibitory neurons","volume":"6","author":"Chamberland","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_57","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1006\/nlme.1998.3842","article-title":"Synaptic plasticity and learning and memory: 15 years of progress","volume":"70","author":"Baudry","year":"1998","journal-title":"Neurobiol. Learn. Mem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.tics.2015.01.002","article-title":"Neural population coding: Combining insights from microscopic and mass signals","volume":"19","author":"Panzeri","year":"2015","journal-title":"Trends Cogn. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s13534-022-00217-z","article-title":"Towards in vivo neural decoding","volume":"12","author":"Valencia","year":"2022","journal-title":"Biomed. Eng. Lett."},{"key":"ref_61","unstructured":"Gerstner, W. (2011). From Neuron to Cognition via Computational Neuroscience, MIT Press."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1038\/nrn1248","article-title":"The other side of the engram: Experience-driven changes in neuronal intrinsic excitability","volume":"4","author":"Zhang","year":"2003","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TNANO.2013.2250995","article-title":"Immunity to device variations in a spiking neural network with memristive nanodevices","volume":"12","author":"Querlioz","year":"2013","journal-title":"IEEE Trans. Nanotechnol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1038\/nn866","article-title":"Parallel processing in high-level categorization of natural images","volume":"5","author":"Rousselet","year":"2002","journal-title":"Nat. Neurosci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.neuron.2012.01.010","article-title":"How does the brain solve visual object recognition?","volume":"73","author":"DiCarlo","year":"2012","journal-title":"Neuron"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1080\/00401706.1973.10489112","article-title":"A generalization of the Poisson distribution","volume":"15","author":"Consul","year":"1973","journal-title":"Technometrics"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.neunet.2019.09.007","article-title":"A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule","volume":"121","author":"Hao","year":"2020","journal-title":"Neural Netw."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.4249\/scholarpedia.3286","article-title":"Neural inhibition","volume":"2","author":"Jonas","year":"2007","journal-title":"Scholarpedia"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1523\/JNEUROSCI.2566-13.2014","article-title":"Rapid dynamic changes of dendritic inhibition in the dentate gyrus by presynaptic activity patterns","volume":"34","author":"Liu","year":"2014","journal-title":"J. Neurosci."},{"key":"ref_70","unstructured":"Widrow, B., Kim, Y., Park, D., and Perin, J.K. (2019). Artificial Intelligence in the Age of Neural Networks and Brain Computing, Elsevier."},{"key":"ref_71","unstructured":"Fatahi, M., Ahmadi, M., Shahsavari, M., Ahmadi, A., and Devienne, P. (2016). evt_MNIST: A spike based version of traditional MNIST. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:36:04Z","timestamp":1760128564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":71,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167232"],"URL":"https:\/\/doi.org\/10.3390\/s23167232","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]}}}