Dataset Information

Long-term memory prepares neural activity for perception.

ABSTRACT: Past experience provides a rich source of predictive information about the world that could be used to guide and optimize ongoing perception. However, the neural mechanisms that integrate information coded in long-term memory (LTM) with ongoing perceptual processing remain unknown. Here, we explore how the contents of LTM optimize perception by modulating anticipatory brain states. By using a paradigm that integrates LTM and attentional orienting, we first demonstrate that the contents of LTM sharpen perceptual sensitivity for targets presented at memory-predicted spatial locations. Next, we examine oscillations in EEG to show that memory-guided attention is associated with spatially specific desynchronization of alpha-band activity over visual cortex. Additionally, we use functional MRI to confirm that target-predictive spatial information stored in LTM triggers spatiotopic modulation of preparatory activity in extrastriate visual cortex. Finally, functional MRI results also implicate an integrated cortical network, including the hippocampus and a dorsal frontoparietal circuit, as a likely candidate for organizing preparatory states in visual cortex according to the contents of LTM.

SUBMITTER: Stokes MG

PROVIDER: S-EPMC3277524 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Publications

Long-term memory prepares neural activity for perception.

Stokes Mark G MG Atherton Kathryn K Patai Eva Zita EZ Nobre Anna Christina AC

Proceedings of the National Academy of Sciences of the United States of America 20111122 6

Past experience provides a rich source of predictive information about the world that could be used to guide and optimize ongoing perception. However, the neural mechanisms that integrate information coded in long-term memory (LTM) with ongoing perceptual processing remain unknown. Here, we explore how the contents of LTM optimize perception by modulating anticipatory brain states. By using a paradigm that integrates LTM and attentional orienting, we first demonstrate that the contents of LTM sh ...[more]

PMID: 22109554

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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

Long-term memory prepares neural activity for perception.

Publications

Long-term memory prepares neural activity for perception.

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