Recall of studied material is typically impaired as time between study and test increases. Selective restudy can interrupt such time-dependent forgetting by enhancing recall not only of the restudied but also of the not restudied material. In two experiments, we examined whether this interruption of time-dependent forgetting reflects a transient or more lasting effect on recall performance. We analyzed time-dependent forgetting of studied items right after study and after time-lagged selective restudy. Restudy boosted recall of the not restudied items up to the levels observed directly after study and created a restart of time-dependent forgetting from this enhanced recall level. Critically, the forgetting after restudy was indistinguishable from the forgetting after study, suggesting that restudy induced a reset of recall for the not restudied items. The results are consistent with the idea that restudy reactivates the temporal context during study, thus facilitating recall of the not restudied items. In particular, the findings suggest that such context updating reflects a lasting effect that entails a restart of the original time-dependent forgetting. Results are discussed with respect to recent, similar findings on effects of time-lagged selective retrieval.
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http://dx.doi.org/10.3758/s13423-022-02131-y | DOI Listing |
We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition.
View Article and Find Full Text PDFACS Appl Electron Mater
July 2024
i3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology and CEMOP/UNINOVA, NOVA University Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal.
Optoelectronic memristors based on amorphous oxide semiconductors (AOSs) are promising devices for the development of spiking neural network (SNN) hardware in neuromorphic vision sensors. In such devices, the conductance state can be controlled by both optical and electrical stimuli, while the typical persistent photoconductivity (PPC) of AOS materials can be used to emulate synaptic functions. However, due to the large band gap of these materials, sensitivity to visible light (red/green/blue) is difficult to accomplish, which hinders applications requiring color discrimination.
View Article and Find Full Text PDFACS Nano
June 2024
Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea.
In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide.
View Article and Find Full Text PDFACS Appl Mater Interfaces
April 2024
Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India.
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in TiCT MXene. The presence of a small fraction of Al ions in partially etched MXene (p-TiCT) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-TiCT)-based devices.
View Article and Find Full Text PDFPLoS Comput Biol
December 2023
Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom.
Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes.
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