Neural Comput - A common network architecture efficiently implements a variety of sparsity-based inference problems.

Tópicos

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{ algorithm(1844) comput(1787) effici(935) }
{ model(3404) distribut(989) bayesian(671) }
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{ network(2748) neural(1063) input(814) }
{ problem(2511) optim(1539) algorithm(950) }
{ howev(809) still(633) remain(590) }
{ use(976) code(926) identifi(902) }
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{ system(1976) rule(880) can(841) }
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{ use(2086) technolog(871) perceiv(783) }
{ imag(2830) propos(1344) filter(1198) }
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{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
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{ method(2212) result(1239) propos(1039) }

Resumo

The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the cost functions we examine are approximate l(p) norms (0 = p = 2), modified l(p)-norms, block-l1 norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted l1 algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.

Resumo Limpo

spars code hypothesi generat signific interest comput theoret neurosci communiti remain open question exact quantit form sparsiti penalti implement code rule neural plausibl architectur main contribut work show wide varieti sparsitybas probabilist infer problem propos signal process statist literatur can implement exact common network architectur known local competit algorithm lca among cost function examin approxim lp norm p modifi lpnorm blockl norm reweight algorithm particular interest show signific increas perform reweight l algorithm infer paramet joint dynam system rather use iter approach nativ digit comput architectur

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