Octopamine, the functional analog of noradrenaline, modulates many different behaviors and physiological processes in invertebrates. In the central nervous system, a few octopaminergic neurons project throughout the brain and innervate almost all neuropils. The center of memory formation in insects, the mushroom bodies, receive octopaminergic innervations in all insects investigated so far. Different octopamine receptors, either increasing or decreasing cAMP or calcium levels in the cell, are localized in Kenyon cells, further supporting the release of octopamine in the mushroom bodies. In addition, different mushroom body (MB) output neurons, projection neurons, and dopaminergic PAM cells are targets of octopaminergic neurons, enabling the modulation of learning circuits at different neural sites. For some years, the theory persisted that octopamine mediates rewarding stimuli, whereas dopamine (DA) represents aversive stimuli. This simple picture has been challenged by the finding that DA is required for both appetitive and aversive learning. Furthermore, octopamine is also involved in aversive learning and a rather complex interaction between these biogenic amines seems to modulate learning and memory. This review summarizes the role of octopamine in MB function, focusing on the anatomical principles and the role of the biogenic amine in learning and memory.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11199948 | PMC |
http://dx.doi.org/10.1101/lm.053839.123 | DOI Listing |
Sensors (Basel)
December 2024
Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain.
View Article and Find Full Text PDFSensors (Basel)
December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification.
View Article and Find Full Text PDFSensors (Basel)
December 2024
B-DAT and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Existing learning-based remote sensing change detection (RSCD) commonly uses semantic-agnostic binary masks as supervision, which hinders their ability to distinguish between different semantic types of changes, resulting in a noisy change mask prediction. To address this issue, this paper presents a Language-guided semantic clustering framework that can effectively transfer the rich semantic information from the contrastive language-image pretraining (CLIP) model for RSCD, dubbed LSC-CD. The LSC-CD considers the strong zero-shot generalization of the CLIP, which makes it easy to transfer the semantic knowledge from the CLIP into the CD model under semantic-agnostic binary mask supervision.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!