Abstract
Working memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics; by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing. Interestingly, only the combination of attractor and transient dynamics enables the network to perform with a low error rate. Further analysis reveals that the transient dynamics of the system are used to process information, while the attractor states store it. The interaction between both types of dynamics yields experimentally testable predictions and we show that this way the system can reliably interact with a timing-unreliable Hebbian-network representing long-term memory. Thus, this study provides a potential solution to the long-standing problem of the basic neuronal dynamics underlying working memory.
Highlights
To resolve this contradiction, in this study, we consider the fact that the timing of stimuli received by the working memory (WM) is highly unreliable
A further analysis reveals that the underlying neuronal dynamics of the trained system are dominated by attractor states which are interlinked by regions of transient dynamics
Stimuli received by the working memory (WM), coming from the environment as well as from the long-term memory (LTM), are characterized by an unreliable timing of their occurrence
Summary
In this study, we consider the fact that the timing of stimuli received by the WM is highly unreliable. Whether the stimuli are presented with exact inter-stimulus timing or with unreliable timing does not influence the subject’s performance of solving the N-back task[22] This result indicates that the mechanisms implementing WM are robust against variance in the timing of the input stimuli. A further analysis reveals that the underlying neuronal dynamics of the trained system are dominated by attractor states which are interlinked by regions of transient dynamics By comparing these combined dynamics with the dynamics of the purely transient system during performing the N-back task, we demonstrate that only this combination of attractor and transient dynamics allows the execution of the task robust against variances in stimuli timings (Fig. 5). This describes, to our knowledge, the first theoretical model of the functional, dynamic interaction between a WM- and a LTM-network
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