Dataset Information

Short- and long-term memory of moving amoeboid cells.

ABSTRACT: Amoeboid cells constantly change shape and extend protrusions. The direction of movement is not random, but is correlated with the direction of movement in the preceding minutes. The basis of this correlation is an underlying memory of direction. The presence of memory in movement is known for many decades, but its molecular mechanism is still largely unknown. This study reports in detail on the information content of directional memory, the kinetics of learning and forgetting this information, and the molecular basis for memory using Dictyostelium mutants. Two types of memory were characterized. A short-term memory stores for ~20 seconds the position of the last pseudopod using a local modification of the branched F-actin inducer SCAR/WAVE, which enhances one new pseudopod to be formed at the position of the previous pseudopod. A long term memory stores for ~2 minutes the activity of the last ~10 pseudopods using a cGMP-binding protein that induces myosin filaments in the rear of the cell; this inhibits pseudopods in the rear and thereby enhances pseudopods in the global front. Similar types of memory were identified in human neutrophils and mesenchymal stem cells, the protist Dictyostelium and the fungus B.d. chytrid. The synergy of short- and long-term memory explains their role in persistent movement for enhanced cell dispersal, food seeking and chemotaxis.

SUBMITTER: van Haastert PJM

PROVIDER: S-EPMC7877599 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Short- and long-term memory of moving amoeboid cells.

van Haastert Peter J M PJM

PloS one 20210211 2

Amoeboid cells constantly change shape and extend protrusions. The direction of movement is not random, but is correlated with the direction of movement in the preceding minutes. The basis of this correlation is an underlying memory of direction. The presence of memory in movement is known for many decades, but its molecular mechanism is still largely unknown. This study reports in detail on the information content of directional memory, the kinetics of learning and forgetting this information, ...[more]

PMID: 33571271

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Project description:A distinction has always been made between long-term and short-term memory (also now called working memory, WM). The obvious difference between these two kinds of memory concerns the duration of information storage: information is supposedly transiently stored in WM while it is considered durably consolidated into long-term memory. It is well acknowledged that the content of WM is erased and reset after a short time, to prevent irrelevant information from proactively interfering with newly stored information. In the present study, we used typical WM radial maze tasks to question the brief lifespan of spatial WM content in rodents. Groups of rats were submitted to one of two different WM tasks in a radial maze: a WM task involving the repetitive presentation of a same pair of arms expected to induce a high level of proactive interference (PI) (HIWM task), or a task using a different pair in each trial expected to induce a low level of PI (LIWM task). Performance was effectively lower in the HIWM group than in LIWM in the final trial of each training session, indicative of a "within-session/short-term" PI effect. However, we also observed a different "between-session/long-term" PI effect between the two groups: while performance of LIWM trained rats remained stable over days, the performance of HIWM rats dropped after 10 days of training, and this impairment was visible from the very first trial of the day, hence not attributable to within-session PI. We also showed that a 24 hour-gap across training sessions known to allow consolidation processes to unfold, was a necessary and sufficient condition for the long-term PI effect to occur. These findings suggest that in the HIWM task, WM content was not entirely reset between training sessions and that, in specific conditions, WM content can outlast its purpose by being stored more permanently, generating a long-term deleterious effect of PI. The alternative explanation is that WM content could be transferred and stored more permanently in an intermediary form or memory between WM and long-term memory.

Project description:BackgroundThe accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.MethodsFirst, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.ResultsThe proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.ConclusionsThe presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Dataset Information

Short- and long-term memory of moving amoeboid cells.

Publications

Short- and long-term memory of moving amoeboid cells.

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