Neural Comput - Detection of hidden structures in nonstationary spike trains.

Tópicos

{ state(1844) use(1261) util(961) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(1737) use(1416) pattern(1282) }
{ estim(2440) model(1874) function(577) }
{ network(2748) neural(1063) input(814) }
{ chang(1828) time(1643) increas(1301) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ concept(1167) ontolog(924) domain(897) }
{ measur(2081) correl(1212) valu(896) }
{ learn(2355) train(1041) set(1003) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
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{ result(1111) use(1088) new(759) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ problem(2511) optim(1539) algorithm(950) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
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{ studi(1410) differ(1259) use(1210) }
{ perform(1367) use(1326) method(1137) }
{ model(3480) simul(1196) paramet(876) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
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{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
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{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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{ use(1733) differ(960) four(931) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.

Resumo Limpo

propos algorithm simultan estim state transit among neural state nonstationari fire rate use switch statespac model sssm algorithm enabl us detect state transit basi discontinu chang mean fire rate also discontinu chang tempor profil fire rate eg tempor correl construct estim learn algorithm nongaussian sssm whose nongaussian properti caus binari spike event local variat method can transform binari observ process quadrat form transform observ process enabl us construct variat bay algorithm can determin number neural state base automat relev determin addit algorithm can estim model paramet singletri data use priori knowledg state transit fire rate synthet data analysi reveal algorithm higher perform estim nonstationari fire rate previous method analysi also confirm algorithm can detect state transit basi discontinu chang tempor correl transit previous hidden markov model detect also analyz neural data record medial tempor area statist detect neural state probabl coincid transient sustain state detect heurist estim paramet suggest algorithm detect state transit basi discontinu chang tempor correl fire rate result suggest algorithm advantag realdata analysi

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