Abstract
Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state.
Highlights
The dominant view of the neural mechanism underlying working memory is that memory representations are maintained in the sustained spiking activity of neurons (Chaudhuri & Fiete, 2016; Bays, 2015; Eriksson, Vogel, Lansner, Bergström, & Nyberg, 2015; Funahashi, Bruce, & Goldman-Rakic, 1989; Fuster & Alexander, 1971)
Focusing on the study by Sprague et al (2016), we demonstrate that the neural model can reproduce both the behavioral and fMRI reconstruction results, despite relying only on active memory representations
We applied a neural model of working memory based on sustained activity in neural populations to emulate the retro-cue task used by Sprague et al (2016)
Summary
The dominant view of the neural mechanism underlying working memory is that memory representations are maintained in the sustained spiking activity of neurons (Chaudhuri & Fiete, 2016; Bays, 2015; Eriksson, Vogel, Lansner, Bergström, & Nyberg, 2015; Funahashi, Bruce, & Goldman-Rakic, 1989; Fuster & Alexander, 1971) This sustained activity may arise from local self-excitation in populations of neurons or reverberatory loops between different cortical areas ( Wang, 2001). Errors arise at encoding and when the attractors drift to neighboring feature values or decay under the influence of random noise ( Wimmer, Nykamp, Constantinidis, & Compte, 2014; Burak & Fiete, 2012; Camperi & Wang, 1998) Models of this type have successfully accounted for memory-related neural activity at a high level of physiological detail (Wimmer et al, 2014; Compte et al, 2000). They have been used to explain a wide range of behavioral findings, such as performance and capacity limits in change detection tasks
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