Neural Comput - Subdiffusive dynamics of bump attractors: mechanisms and functional roles.

Tópicos

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Resumo

Bump attractors are localized activity patterns that can self-sustain after stimulus presentation, and they are regarded as the neural substrate for a host of perceptual and cognitive processes. One of the characteristic features of bump attractors is that they are neutrally stable, so that noisy inputs cause them to drift away from their initial locations, severely impairing the accuracy of bump location-dependent neural coding. Previous modeling studies of such noise-induced drifting activity of bump attractors have focused on normal diffusive dynamics, often with an assumption that noisy inputs are uncorrelated. Here we show that long-range temporal correlations and spatial correlations in neural inputs generated by multiple interacting bumps cause them to drift in an anomalous subdiffusive way. This mechanism for generating subdiffusive dynamics of bump attractors is further analyzed based on a generalized Langevin equation. We demonstrate that subdiffusive dynamics can significantly improve the coding accuracy of bump attractors, since the variance of the bump displacement increases sublinearly over time and is much smaller than that of normal diffusion. Furthermore, we reanalyze existing psychophysical data concerning the spread of recalled cue position in spatial working memory tasks and show that its variance increases sublinearly with time, consistent with subdiffusive dynamics of bump attractors. Based on the probability density function of bump position, we also show that the subdiffusive dynamics result in a long-tailed decay of firing rate, greatly extending the duration of persistent activity.

Resumo Limpo

bump attractor local activ pattern can selfsustain stimulus present regard neural substrat host perceptu cognit process one characterist featur bump attractor neutral stabl noisi input caus drift away initi locat sever impair accuraci bump locationdepend neural code previous model studi noiseinduc drift activ bump attractor focus normal diffus dynam often assumpt noisi input uncorrel show longrang tempor correl spatial correl neural input generat multipl interact bump caus drift anomal subdiffus way mechan generat subdiffus dynam bump attractor analyz base general langevin equat demonstr subdiffus dynam can signific improv code accuraci bump attractor sinc varianc bump displac increas sublinear time much smaller normal diffus furthermor reanalyz exist psychophys data concern spread recal cue posit spatial work memori task show varianc increas sublinear time consist subdiffus dynam bump attractor base probabl densiti function bump posit also show subdiffus dynam result longtail decay fire rate great extend durat persist activ

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