{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:29:05Z","timestamp":1785414545721,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62173073"],"award-info":[{"award-number":["62173073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["62173073"],"award-info":[{"award-number":["62173073"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013099","name":"Scientific Research Fund of Liaoning Provincial Education Department","doi-asserted-by":"crossref","award":["JYTMS20230215"],"award-info":[{"award-number":["JYTMS20230215"]}],"id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Excitatory\u2013inhibitory (E-I) local circuits play a central role in synaptic plasticity and neural coding, yet their multilayer learning dynamics remain poorly understood. We constructed a multilayer feedforward spiking neural network with intra-layer E-I connectivity, using Izhikevich neurons to model regular spiking (RS) and fast spiking (FS) cells, and examined cooperative learning under excitatory and inhibitory spike-timing-dependent plasticity (eSTDP and iSTDP). FS-mediated lateral inhibition alleviates the long-term depression bias arising from RS firing rate adaptation via winner-take-all competition, promoting heterogeneous E\u2192E weight differentiation while preserving mean synaptic strength. A 12\u00d712 parameter grid scan shows that iSTDP expands the stable learning region in the E-I parameter space and reveals a sustained cooperative co-evolution of eSTDP and iSTDP during training. For sparse coding, RS adaptation is the primary driver of Lifetime Sparseness, with FS inhibition acting as a cooperative enhancer; the network exhibits low sparseness at the input layer, a rapid increase at the second layer, and a stable plateau in deeper layers. For temporal pattern learning, the selectivity index d\u2032 improved substantially after training, reaching approximately 1.90 times that of the FS-absent condition; both interval sensitivity and pattern generalization tests confirmed that this advantage is robust across biologically plausible inter-group delays and preserved under small temporal jitter. Mutual information analysis reveals a consistent tendency for intra-layer FS circuits to maintain higher stimulus-related information across deep layers, consistent with FS-mediated suppression of non-specific responses. These findings provide computational evidence, within the scope of the present model, for understanding cortical E-I cooperative plasticity and inform design principles for neuromorphic systems with adaptive inhibitory regulation.<\/jats:p>","DOI":"10.3390\/biomimetics11070462","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:43:14Z","timestamp":1783003394000},"page":"462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparse Coding and Temporal Pattern Learning Co-Mediated by Dual Spike-Timing-Dependent Plasticity in a Multilayer Excitatory\u2013Inhibitory Spiking Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9633-5694","authenticated-orcid":false,"given":"Chunhua","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianwen","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1111\/ejn.13595","article-title":"Neurobiological bases of autism-epilepsy comorbidity: A focus on excitation\/inhibition imbalance","volume":"47","author":"Bozzi","year":"2018","journal-title":"Eur. J. Neurosci."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3571155","article-title":"Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware","volume":"55","author":"Rathi","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1162\/neco_a_01499","article-title":"Advancements in algorithms and neuromorphic hardware for spiking neural networks","volume":"34","author":"Javanshir","year":"2022","journal-title":"Neural Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5387","DOI":"10.1109\/TCYB.2025.3591897","article-title":"T3-ANFIS: Type-3 Adaptive Neuro-Fuzzy Inference System With a Noniterative Learning Algorithm","volume":"55","author":"Mohammadzadeh","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neunet.2017.12.005","article-title":"STDP-based spiking deep convolutional neural networks for object recognition","volume":"99","author":"Kheradpisheh","year":"2018","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"She, X., Dash, S., Kim, D., and Mukhopadhyay, S. (2021). A heterogeneous spiking neural network for unsupervised learning of spatiotemporal patterns. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.615756"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1038\/78829","article-title":"Competitive Hebbian learning through spike-timing-dependent synaptic plasticity","volume":"3","author":"Song","year":"2000","journal-title":"Nat. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/S0896-6273(01)00451-2","article-title":"Cortical development and remapping through spike timing-dependent plasticity","volume":"32","author":"Song","year":"2001","journal-title":"Neuron"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.neuron.2018.07.018","article-title":"Parvalbumin-interneuron output synapses show spike-timing-dependent plasticity that contributes to auditory map remodeling","volume":"99","author":"Vickers","year":"2018","journal-title":"Neuron"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1038\/nrn1519","article-title":"Interneurons of the neocortical inhibitory system","volume":"5","author":"Markram","year":"2004","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/0166-2236(90)90185-D","article-title":"Intrinsic firing patterns of diverse neocortical neurons","volume":"13","author":"Connors","year":"1990","journal-title":"Trends Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rupert, D.D., and Shea, S.D. (2022). Parvalbumin-positive interneurons regulate cortical sensory plasticity in adulthood and development through shared mechanisms. Front. Neural Circuits, 16.","DOI":"10.3389\/fncir.2022.886629"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1146\/annurev-neuro-072116-031005","article-title":"Inhibitory plasticity: Balance, control, and codependence","volume":"40","author":"Hennequin","year":"2017","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1146\/annurev-neuro-071714-034002","article-title":"Plasticity of cortical excitatory-inhibitory balance","volume":"38","author":"Froemke","year":"2015","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1126\/science.1211095","article-title":"Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks","volume":"334","author":"Vogels","year":"2011","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luz, Y., and Shamir, M. (2012). Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002334"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.cell.2008.10.008","article-title":"The self-tuning neuron: Synaptic scaling of excitatory synapses","volume":"135","author":"Turrigiano","year":"2008","journal-title":"Cell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/S0893-6080(05)80154-6","article-title":"Winner-take-all networks for physiological models of competitive learning","volume":"7","author":"Kaski","year":"1994","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ferr\u00e9, P., Mamalet, F., and Thorpe, S.J. (2018). Unsupervised feature learning with winner-takes-all based STDP. Front. Comput. Neurosci., 12.","DOI":"10.3389\/fncom.2018.00024"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/381607a0","article-title":"Emergence of simple-cell receptive field properties by learning a sparse code for natural images","volume":"381","author":"Olshausen","year":"1996","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","article-title":"Sparse coding with an overcomplete basis set: A strategy employed by V1?","volume":"37","author":"Olshausen","year":"1997","journal-title":"Vis. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1068\/p010371","article-title":"Single units and sensation: A neuron doctrine for perceptual psychology?","volume":"1","author":"Barlow","year":"1972","journal-title":"Perception"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1080\/net.12.3.255.270","article-title":"Characterizing the sparseness of neural codes","volume":"12","author":"Willmore","year":"2001","journal-title":"Netw. Comput. Neural Syst."},{"key":"ref_27","unstructured":"Green, D.M., and Swets, J.A. (1966). Signal Detection Theory and Psychophysics, Wiley."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/14731","article-title":"Information theory and neural coding","volume":"2","author":"Borst","year":"1999","journal-title":"Nat. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11515","DOI":"10.1523\/JNEUROSCI.5044-12.2013","article-title":"Emergence of dynamic memory traces in cortical microcircuit models through STDP","volume":"33","author":"Klampfl","year":"2013","journal-title":"J. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5319","DOI":"10.1038\/ncomms6319","article-title":"Formation and maintenance of neuronal assemblies through synaptic plasticity","volume":"5","author":"Doiron","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","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"}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/7\/462\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T05:00:36Z","timestamp":1783141236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/7\/462"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["biomimetics11070462"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11070462","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]}}}