While many theories assume that sleep is critical in stabilizing and strengthening memories, our recent behavioral study (Liu & Ranganath, 2021, Psychonomic Bulletin & Review, 28[6], 2035-2044) suggests that sleep does not simply stabilize memories. Instead, it plays a more complex role, integrating information across two temporally distinct learning episodes. In the current study, we simulated the results of Liu and Ranganath (2021) using our biologically plausible computational model, TEACH, developed based on the complementary learning systems (CLS) framework. Our model suggests that when memories are activated during sleep, the reduced influence of temporal context establishes connections across temporally separated events through mutual training between the hippocampus and neocortex. In addition to providing a compelling mechanistic explanation for the selective effect of sleep, this model offers new examples of the diverse ways in which the cortex and hippocampus can interact during learning.
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http://dx.doi.org/10.3758/s13423-024-02489-1 | DOI Listing |
Philos Trans A Math Phys Eng Sci
January 2025
Microsystems Group, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Materials and Energy, Lanzhou University (LZU), Lanzhou 730000, China.
Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping.
View Article and Find Full Text PDFHandb Exp Pharmacol
January 2025
Genentech Inc, South San Francisco, CA, USA.
In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI.
View Article and Find Full Text PDFElife
January 2025
Department of Neurology, University of Iowa, Iowa City, United States.
The role of striatal pathways in cognitive processing is unclear. We studied dorsomedial striatal cognitive processing during interval timing, an elementary cognitive task that requires mice to estimate intervals of several seconds and involves working memory for temporal rules as well as attention to the passage of time. We harnessed optogenetic tagging to record from striatal D2-dopamine receptor-expressing medium spiny neurons (D2-MSNs) in the indirect pathway and from D1-dopamine receptor-expressing MSNs (D1-MSNs) in the direct pathway.
View Article and Find Full Text PDFData Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
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