Response and place memory systems have long been considered independent, encoding information in parallel, and involving the striatum and hippocampus, respectively. Most experimental studies supporting this view used simple, repetitive tasks, with unrestrained access to spatial cues. They did not give animals an opportunity to correct a response strategy by shifting to a place one, which would demonstrate dynamic, adaptive interactions between both memory systems in the navigation correction process. In a first experiment, rats were trained in the double-H maze for different durations (1, 6, or 14 days; 4 trials/day) to acquire a repetitive task in darkness (forcing a response memory-based strategy) or normal light (placing response and place memory systems in balance), or to acquire a place memory. All rats were given a misleading shifted-start probe trial 24-h post-training to test both their strategy and their ability to correct their navigation directly or in response to negative feedback. Additional analyses focused on the dorsal striatum and the dorsal hippocampus using c-Fos gene expression imaging and, in a second experiment, reversible muscimol inactivation. The results indicate that, depending on training protocol and duration, the striatum, which was unexpectedly the first to come into play in the dual strategy task, and the hippocampus are both required when rats have to correct their navigation after having acquired a repetitive task in a cued environment. Partly contradicting the model established by Packard and McGaugh (1996, Neurobiology of Learning and Memory, vol. 65), these data point to memory systems that interact in more complex ways than considered so far. To some extent, they also challenge the notion of hippocampus-independent response memory and striatum-independent place memory systems.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.nlm.2019.107131 | DOI Listing |
JMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFNeurology
February 2025
Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, the Netherlands.
Background And Objectives: Identifying genetic causes of dementia in patients visiting memory clinics is important for patient care and family planning. Traditional clinical selection criteria for genetic testing may miss carriers of pathogenic variants in dementia-related genes. This study aimed identify how many carriers we are missing and to optimize criteria for selecting patients for genetic counseling in memory clinics.
View Article and Find Full Text PDFPsychol Rev
January 2025
Department of Cognitive Science, University of California, San Diego.
It has long been hypothesized that episodic memory supports adaptive decision making by enabling mental simulation of future events. Yet, attempts to characterize this process are surprisingly rare. On one hand, memory research is often carried out in settings that are far removed from ecological contexts of decision making.
View Article and Find Full Text PDFPLoS One
January 2025
School of Information and Communication Engineering, Beijing University of Information Science and Technology, Bei Jing City, China.
To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!