{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:43:41Z","timestamp":1790185421082,"version":"4.1.0"},"reference-count":36,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"5","funder":[{"name":"ENS"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Appl. Math."],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>In neuroscience, learning and memory are usually associated with long-term changes in neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called synapses and represented by a scalar value, the synaptic weight. Spike-timing-dependent plasticity (STDP) is a biologically based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons. Although numerous models of neuronal cells have been proposed in the mathematical literature, few of them include a variable for the time-varying strength of the connection. A new, general, mathematical framework is introduced to study synaptic plasticity associated to different STDP rules. The system composed of two neurons connected by a single synapse is investigated, and a stochastic process describing its dynamical behavior is presented and analyzed. The notion of plasticity kernel is introduced as a key component of plastic neural networks models, generalizing a notion used for pair-based models. We show that a large number of STDP rules from neuroscience and physics can be represented by this formalism. Several aspects of these models are discussed and compared to canonical models of computational neuroscience. An important subclass of plasticity kernels with a Markovian formulation is also defined and investigated. In these models, the time evolution of cellular processes such as the neuronal membrane potential and the concentrations of chemical components created\/suppressed by spiking activity has the Markov property.<\/jats:p>","DOI":"10.1137\/20m138288x","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T10:19:15Z","timestamp":1631182755000},"page":"1821-1846","source":"Crossref","is-referenced-by-count":14,"title":["Stochastic Models of Neural Synaptic Plasticity"],"prefix":"10.1137","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8122-8880","authenticated-orcid":true,"given":"Philippe","family":"Robert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4115-4017","authenticated-orcid":true,"given":"Ga\u00ebtan","family":"Vignoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004750"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998"},{"key":"atypb3","doi-asserted-by":"crossref","unstructured":"P. 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Dawson,\n                      Measure-valued Markov processes\n                      , in \u00c9cole d'\u00c9t\u00e9 de Probabilit\u00e9s de Saint-Flour XXI-1991, Lecture Notes in Math. 1541, Springer, Berlin, 1993, pp. 1-260.","DOI":"10.1007\/BFb0084190"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.08.001"},{"key":"atypb10","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4476-05.2005"},{"key":"atypb11","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.6249-09.2010"},{"key":"atypb12","doi-asserted-by":"crossref","unstructured":"W. Gerstner, W. M. Kistler, R. Naud, and L. Paninski,\n                      Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition\n                      , Cambridge University Press, New York, 2014.","DOI":"10.1017\/CBO9781107447615"},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2010.00136"},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1109359109"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.23-09-03697.2003"},{"key":"atypb16","unstructured":"P. Helson,\n                      A New Stochastic STDP Rule in a Neural Network Model\n                      , preprint,http:\/\/arxiv.org\/abs\/1706.00364, 2018 (accessed 2019-11-26)."},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.59.4498"},{"key":"atypb18","doi-asserted-by":"crossref","unstructured":"J. F. C. Kingman,\n                      Poisson Processes\n                      , Clarendon Press, New York, 1992.","DOI":"10.1093\/oso\/9780198536932.001.0001"},{"key":"atypb19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2008.01.036"},{"key":"atypb20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0101109"},{"key":"atypb21","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2007.19.6.1437"},{"key":"atypb22","doi-asserted-by":"publisher","DOI":"10.1007\/s00422-008-0233-1"},{"key":"atypb23","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1425-06.2006"},{"key":"atypb24","unstructured":"P. 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