{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:26:01Z","timestamp":1740151561149,"version":"3.37.3"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2012,10,10]],"date-time":"2012-10-10T00:00:00Z","timestamp":1349827200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["0925407"],"award-info":[{"award-number":["0925407"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Artificial Neural Systems"],"published-print":{"date-parts":[[2012,10,10]]},"abstract":"<jats:p>This paper presents a deterministic and adaptive spike model derived from radial basis functions\nand a leaky integrate-and-fire sampler developed for training spiking neural networks without direct\nweight manipulation. Several algorithms have been proposed for training spiking neural networks\nthrough biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity\nand Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths,\nor weights, directly, through a weight update rule in which the weight increment can be decided\nand implemented based on the training equations. However, in several potential applications of\nadaptive spiking neural networks, including neuroprosthetic devices and CMOS\/memristor nanoscale\nneuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time\nby virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method\nthat induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means\nof controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains\nused to stimulate the input neurons by determining a sequence of spike timings that minimize a desired\nobjective function and, indirectly, induce the desired synaptic plasticity in the network.<\/jats:p>","DOI":"10.1155\/2012\/713581","type":"journal-article","created":{"date-parts":[[2012,10,11]],"date-time":"2012-10-11T11:17:21Z","timestamp":1349954241000},"page":"1-16","source":"Crossref","is-referenced-by-count":7,"title":["A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques"],"prefix":"10.1155","volume":"2012","author":[{"given":"X.","family":"Zhang","sequence":"first","affiliation":[{"name":"Laboratory for Intelligent Systems and Control (LISC), Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA"}]},{"given":"G.","family":"Foderaro","sequence":"additional","affiliation":[{"name":"Laboratory for Intelligent Systems and Control (LISC), Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA"}]},{"given":"C.","family":"Henriquez","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and  Department of Computer Science, Duke University Durham, NC 27708, USA"}]},{"given":"A. 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