Left: A stable coding scheme within a stable neural population (defined by dimensions 1 and 2 dimension 3 has no meaningful variance). ![]() (A) The relationship between the neural coding scheme of orientations (colours) in WM over time, illustrated in neural state space (reduced to three dimensions, for visualisation). Such dynamics emerge naturally in a recurrent network and provide rich information about the previous input and elapsed time but necessarily entail a more complex readout strategy (i.e., time-specific decoders or a high-dimensional classifier that finds a high-dimensional hyperplane that separates memory condition for all time points ). Although the relationship between activity pattern and memory content changes over time, the representational geometry could remain relatively constant. Finally, it is also possible to maintain stable information in a richer dynamical system (e.g., ). As in the original stable attractor model, the coding scheme is stable over time, permitting easy and unambiguous WM readout by downstream systems, regardless of maintenance duration. This permits some dynamic activity whilst also maintaining a fixed coding relationship of WM content over time. For example, in a recent hybrid model, stable attractor dynamics coexist with a low-dimensional, time-varying component (, see Fig 1A for model schematics). However, more dynamic models have also been suggested. This solution has been well studied and provides a simple readout of memory content irrespective of time (i.e., memory delay). Working memory (WM) is a core cognitive function that provides a stable platform for guiding behaviour according to time-extended goals however, it remains unclear how such stable cognitive states emerge from a dynamic neural system.Īt one extreme, WM could effectively pause the inherent dynamics by falling into a stable attractor (e.g., ). Neural activity is highly dynamic, yet often we need to hold information in mind in a stable state to guide ongoing behaviour. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research or the Department of Health. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). McDonnell Foundation Scholar Award (220020405) to MGS and an Open Research Area grant to MGS (ESRC ES/S015477/1) and EGA (NWO 464.18.114). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All data and custom Matlab scripts used to generate the results and figures of this manuscript are available from the OSF database ( osf.io/cn8zf).įunding: This research was in part funded by a James S. Received: JAccepted: FebruPublished: March 2, 2020Ĭopyright: © 2020 Wolff et al. PLoS Biol 18(3):Īcademic Editor: Frank Tong, Vanderbilt University, UNITED STATES When averaged across trials, such drift contributes to the width of the error distribution.Ĭitation: Wolff MJ, Jochim J, Akyürek EG, Buschman TJ, Stokes MG (2020) Drifting codes within a stable coding scheme for working memory. ![]() Indeed, we find that even within the stable coding scheme, memories drift during maintenance. Despite having a stable subspace, WM is clearly not perfect-memory performance still degrades over time. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorised orientations was stable. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. Working memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment.
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