Neural Comput - Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function.

Tópicos

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{ imag(2830) propos(1344) filter(1198) }
{ first(2504) two(1366) second(1323) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ use(1733) differ(960) four(931) }
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{ result(1111) use(1088) new(759) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A method is presented for learning the reciprocal feedforward and feedback connections required by the predictive coding model of cortical function. When this method is used, feedforward and feedback connections are learned simultaneously and independently in a biologically plausible manner. The performance of the proposed algorithm is evaluated by applying it to learning the elementary components of artificial and natural images. For artificial images, the bars problem is employed, and the proposed algorithm is shown to produce state-of-the-art performance on this task. For natural images, components resembling Gabor functions are learned in the first processing stage, and neurons responsive to corners are learned in the second processing stage. The properties of these learned representations are in good agreement with neurophysiological data from V1 and V2. The proposed algorithm demonstrates for the first time that a single computational theory can explain the formation of cortical RFs and also the response properties of cortical neurons once those RFs have been learned.

Resumo Limpo

method present learn reciproc feedforward feedback connect requir predict code model cortic function method use feedforward feedback connect learn simultan independ biolog plausibl manner perform propos algorithm evalu appli learn elementari compon artifici natur imag artifici imag bar problem employ propos algorithm shown produc stateoftheart perform task natur imag compon resembl gabor function learn first process stage neuron respons corner learn second process stage properti learn represent good agreement neurophysiolog data v v propos algorithm demonstr first time singl comput theori can explain format cortic rfs also respons properti cortic neuron rfs learn

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