{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T18:00:13Z","timestamp":1681408813120},"reference-count":20,"publisher":"MIT Press - Journals","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2002,4,1]]},"abstract":"<jats:p> Markram and Tsodyks, by showing that the elevated synaptic efficacy observed with single-pulse long-term potentiation (LTP) measurements disappears with higher-frequency test pulses, have critically challenged the conventional assumption that LTP reflects a general gain increase. This observed change in frequency dependence during synaptic potentiation is called redistribution of synaptic efficacy (RSE). RSE is here seen as the local realization of a global design principle in a neural network for pattern coding. The underlying computational model posits an adaptive threshold rather than a multiplicative weight as the elementary unit of long-term memory. A distributed instar learning law allows thresholds to increase only monotonically, but adaptation has a bidirectional effect on the model postsynaptic potential. At each synapse, threshold increases implement pattern selectivity via a frequency-dependent signal component, while a complementary frequency-independent component nonspecifically strengthens the path. This synaptic balance produces changes in frequency dependence that are robustly similar to those observed by Markram and Tsodyks. The network design therefore suggests a functional purpose for RSE, which, by helping to bound total memory change, supports a distributed coding scheme that is stable with fast as well as slow learning. Multiplicative weights have served as a cornerstone for models of physiological data and neural systems for decades. Although the model discussed here does not implement detailed physiology of synaptic transmission, its new learning laws operate in a network architecture that suggests how recently discovered synaptic computations such as RSE may help produce new network capabilities such as learning that is fast, stable, and distributed. <\/jats:p>","DOI":"10.1162\/089976602317318992","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T11:56:30Z","timestamp":1027770990000},"page":"873-888","source":"Crossref","is-referenced-by-count":6,"title":["Redistribution of Synaptic Efficacy Supports Stable Pattern Learning in Neural Networks"],"prefix":"10.1162","volume":"14","author":[{"given":"Gail A.","family":"Carpenter","sequence":"first","affiliation":[{"name":"Department of Cognitive and Neural Systems, Boston University, Boston, Massachusetts 02215, U.S.A."}]},{"given":"Boriana L.","family":"Milenova","sequence":"additional","affiliation":[{"name":"Department of Cognitive and Neural Systems, Boston University, Boston, Massachusetts 02215, U.S.A."}]}],"member":"281","reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.275.5297.221"},{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)90064-7"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00004-X"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1016\/S1364-6613(00)01591-6"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1016\/S0734-189X(87)80014-2"},{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(90)90085-Y"},{"key":"p_8","doi-asserted-by":"publisher","DOI":"10.1016\/0166-2236(93)90118-6"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1109\/72.159059"},{"key":"p_10","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90012-T"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90056-B"},{"key":"p_12","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00019-7"},{"key":"p_13","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.60.3.758"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1007\/BF00288784"},{"key":"p_15","doi-asserted-by":"publisher","DOI":"10.1016\/S0028-3908(98)00049-5"},{"key":"p_16","doi-asserted-by":"publisher","DOI":"10.1038\/382807a0"},{"key":"p_17","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.95.9.5323"},{"key":"p_18","first-page":"127","volume":"9","author":"McCulloch W. 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