Abstract
Persistent activity observed during delayed-response tasks for spatial working memory (Funahashi et al., 1989) has commonly been modeled by recurrent networks whose dynamics is described as a bump attractor (Compte et al., 2000). We examine the effects of interareal architecture on the dynamics of bump attractors in stochastic neural fields. Lateral inhibitory synaptic structure in each area sustains stationary bumps in the absence of noise. Introducing noise causes bumps in individual areas to wander as a Brownian walk. However, coupling multiple areas together can help reduce the variability of the bump's position in each area. To examine this quantitatively, we approximate the position of the bump in each area using a small noise expansion that also assumes weak amplitude interareal projections. Our asymptotic results show the motion of the bumps in each area can be approximated as a multivariate Ornstein–Uhlenbeck process. This shows reciprocal coupling between areas can always reduce variability, if sufficiently strong, even if one area contains much more noise than the other. However, when noise is correlated between areas, the variability-reducing effect of interareal coupling is diminished. Our results suggest that distributing spatial working memory representations across multiple, reciprocally-coupled brain areas can lead to noise cancelation that ultimately improves encoding.
Highlights
Persistent spiking activity has been experimentally observed in prefrontal cortex (Funahashi et al, 1989; Miller et al, 1996), parietal cortex (Colby et al, 1996; Pesaran et al, 2002), superior colliculus (Basso and Wurtz, 1997), caudate nucleus (Hikosaka et al, 1989; Levy et al, 1997), and globus pallidus (Mushiake and Strick, 1995; McNab and Klingberg, 2008) during the retention interval of visuospatial working memory tasks
We will study how interareal architecture affect the dynamics of bumps in multiple area stochastic neural fields
Since bump attractors offer a well studied model of persistent activity underlying spatial working memory (Compte et al, 2000), our results provide a novel suggestion for how the memory networks may reduce error
Summary
Persistent spiking activity has been experimentally observed in prefrontal cortex (Funahashi et al, 1989; Miller et al, 1996), parietal cortex (Colby et al, 1996; Pesaran et al, 2002), superior colliculus (Basso and Wurtz, 1997), caudate nucleus (Hikosaka et al, 1989; Levy et al, 1997), and globus pallidus (Mushiake and Strick, 1995; McNab and Klingberg, 2008) during the retention interval of visuospatial working memory tasks. Delay period neurons persistently fire in response to a preferred cue orientation as described by a bell-shaped tuning curve. Networks of these neurons, with recurrent excitation between tuned neurons and broadly tuned feedback inhibition, can generate spatially localized “bumps.” The position of these bumps encodes the remembered location of the cue (Compte et al, 2000). Psychophysical data demonstrates spatial working memory error does scale linearly with delay time, suggesting the underlying process that degrades memory is diffusive (White et al, 1994; Ploner et al, 1998). Spatially heterogeneous recurrent excitation can reduce wandering of bumps quantizing the line attractor by stabilizing a finite set of bump locations (Kilpatrick and Ermentrout, 2013; Kilpatrick et al, 2013)
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