Neural Comput - Internal-time temporal difference model for neural value-based decision making.

Tópicos

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{ network(2748) neural(1063) input(814) }
{ clinic(1479) use(1117) guidelin(835) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ concept(1167) ontolog(924) domain(897) }
{ import(1318) role(1303) understand(862) }
{ measur(2081) correl(1212) valu(896) }
{ problem(2511) optim(1539) algorithm(950) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ take(945) account(800) differ(722) }
{ patient(2315) diseas(1263) diabet(1191) }
{ error(1145) method(1030) estim(1020) }
{ signal(2180) analysi(812) frequenc(800) }
{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ method(1557) propos(1049) approach(1037) }
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{ studi(1119) effect(1106) posit(819) }
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{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
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{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
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{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ activ(1452) weight(1219) physic(1104) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The temporal difference (TD) learning framework is a major paradigm for understanding value-based decision making and related neural activities (e.g., dopamine activity). The representation of time in neural processes modeled by a TD framework, however, is poorly understood. To address this issue, we propose a TD formulation that separates the time of the operator (neural valuation processes), which we refer to as internal time, from the time of the observer (experiment), which we refer to as conventional time. We provide the formulation and theoretical characteristics of this TD model based on internal time, called internal-time TD, and explore the possible consequences of the use of this model in neural value-based decision making. Due to the separation of the two times, internal-time TD computations, such as TD error, are expressed differently, depending on both the time frame and time unit. We examine this operator-observer problem in relation to the time representation used in previous TD models. An internal time TD value function exhibits the co-appearance of exponential and hyperbolic discounting at different delays in intertemporal choice tasks. We further examine the effects of internal time noise on TD error, the dynamic construction of internal time, and the modulation of internal time with the internal time hypothesis of serotonin function. We also relate the internal TD formulation to research on interval timing and subjective time.

Resumo Limpo

tempor differ td learn framework major paradigm understand valuebas decis make relat neural activ eg dopamin activ represent time neural process model td framework howev poor understood address issu propos td formul separ time oper neural valuat process refer intern time time observ experi refer convent time provid formul theoret characterist td model base intern time call internaltim td explor possibl consequ use model neural valuebas decis make due separ two time internaltim td comput td error express differ depend time frame time unit examin operatorobserv problem relat time represent use previous td model intern time td valu function exhibit coappear exponenti hyperbol discount differ delay intertempor choic task examin effect intern time nois td error dynam construct intern time modul intern time intern time hypothesi serotonin function also relat intern td formul research interv time subject time

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