Neural Comput - Active data collection for efficient estimation and comparison of nonlinear neural models.

Tópicos

{ model(3480) simul(1196) paramet(876) }
{ perform(1367) use(1326) method(1137) }
{ data(3963) clinic(1234) research(1004) }
{ visual(1396) interact(850) tool(830) }
{ network(2748) neural(1063) input(814) }
{ method(984) reconstruct(947) comput(926) }
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{ result(1111) use(1088) new(759) }
{ estim(2440) model(1874) function(577) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
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{ method(1557) propos(1049) approach(1037) }
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{ group(2977) signific(1463) compar(1072) }
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{ health(1844) social(1437) communiti(874) }
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{ measur(2081) correl(1212) valu(896) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
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{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ sampl(1606) size(1419) use(1276) }
{ use(2086) technolog(871) perceiv(783) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ surgeri(1148) surgic(1085) robot(1054) }
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{ error(1145) method(1030) estim(1020) }
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{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
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{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The stimulus-response relationship of many sensory neurons is nonlinear, but fully quantifying this relationship by a complex nonlinear model may require too much data to be experimentally tractable. Here we present a theoretical study of a general two-stage computational method that may help to significantly reduce the number of stimuli needed to obtain an accurate mathematical description of nonlinear neural responses. Our method of active data collection first adaptively generates stimuli that are optimal for estimating the parameters of competing nonlinear models and then uses these estimates to generate stimuli online that are optimal for discriminating these models. We applied our method to simple hierarchical circuit models, including nonlinear networks built on the spatiotemporal or spectral-temporal receptive fields, and confirmed that collecting data using our two-stage adaptive algorithm was far more effective for estimating and comparing competing nonlinear sensory processing models than standard nonadaptive methods using random stimuli.

Resumo Limpo

stimulusrespons relationship mani sensori neuron nonlinear fulli quantifi relationship complex nonlinear model may requir much data experiment tractabl present theoret studi general twostag comput method may help signific reduc number stimuli need obtain accur mathemat descript nonlinear neural respons method activ data collect first adapt generat stimuli optim estim paramet compet nonlinear model use estim generat stimuli onlin optim discrimin model appli method simpl hierarch circuit model includ nonlinear network built spatiotempor spectraltempor recept field confirm collect data use twostag adapt algorithm far effect estim compar compet nonlinear sensori process model standard nonadapt method use random stimuli

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