Neural Comput - Neural representation of spatial topology in the rodent hippocampus.

Tópicos

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{ state(1844) use(1261) util(961) }
{ structur(1116) can(940) graph(676) }
{ perform(999) metric(946) measur(919) }
{ studi(1119) effect(1106) posit(819) }
{ activ(1452) weight(1219) physic(1104) }
{ featur(3375) classif(2383) classifi(1994) }
{ use(976) code(926) identifi(902) }
{ model(2220) cell(1177) simul(1124) }
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{ 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) }
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{ first(2504) two(1366) second(1323) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater detail. We recorded ensembles of hippocampal neurons as rodents freely foraged in one- and two-dimensional spatial environments and used a "decode-to-uncover" strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations ("states") were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one- and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, examine hippocampal population codes during off-line states, and quantify the topological complexity of the environment.

Resumo Limpo

pyramid cell rodent hippocampus often exhibit clear spatial tune navig although long suggest pyramid cell activ may underli topolog code rather topograph code remain unclear whether abstract spatial topolog can encod ensembl spike activ hippocamp place cell use statist approach develop previous investig question relat issu greater detail record ensembl hippocamp neuron rodent freeli forag one twodimension spatial environ use decodetouncov strategi examin tempor structur pattern embed ensembl spike activ absenc observ spatial correl period rodent navig awak immobl specif spatial environ repres finit discret state space trajectori across spatial locat state associ consist hippocamp ensembl spike pattern character state transit matrix state transit matrix infer topolog graph defin connect state space one twodimension environ extract behavior pattern rodent hippocamp popul code compar random shuffl spike data contrast topograph code result support effici topolog code presenc spars sampl size fuzzi space map comput approach allow us quantifi variabl ensembl spike activ examin hippocamp popul code offlin state quantifi topolog complex environ

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