Neural Comput - Sequence memory based on coherent spin-interaction neural networks.

Tópicos

{ network(2748) neural(1063) input(814) }
{ sequenc(1873) structur(1644) protein(1328) }
{ data(1737) use(1416) pattern(1282) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ estim(2440) model(1874) function(577) }
{ measur(2081) correl(1212) valu(896) }
{ framework(1458) process(801) describ(734) }
{ import(1318) role(1303) understand(862) }
{ result(1111) use(1088) new(759) }
{ control(1307) perform(991) simul(935) }
{ can(981) present(881) function(850) }
{ featur(3375) classif(2383) classifi(1994) }
{ care(1570) inform(1187) nurs(1089) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ inform(2794) health(2639) internet(1427) }
{ method(1219) similar(1157) match(930) }
{ extract(1171) text(1153) clinic(932) }
{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
{ model(3480) simul(1196) paramet(876) }
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{ activ(1452) weight(1219) physic(1104) }
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{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

Resumo Limpo

sequenc inform process instanc sequenc memori play import role mani function brain work human brain steadyst period alter howev exist sequenc memori model use heteroassoci steadyst period chang sequenc recal work novel neural network model sequenc memori control steadyst period base coher spininteract propos propos model neuron fire collect phasecoher manner let neuron group respond differ differ pattern also let differ neuron group respond differ one pattern simul result demonstr perform sequenc memori present introduc new coher spininteract sequenc memori model steadyst period can control dimens paramet overlap input pattern store pattern sequenc storag capac enlarg coher spin interact compar exist sequenc memori model furthermor sequenc storag capac exponenti relationship dimens neural network

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