Here we describe several fundamental principles of olfactory processing in the Drosophila melanogaster antennal lobe (the analog of the vertebrate olfactory bulb), through the systematic analysis of input and output spike trains of seven identified glomeruli. Repeated presentations of the same odor elicit more reproducible responses in second-order projection neurons (PNs) than in their presynaptic olfactory receptor neurons (ORNs). PN responses rise and accommodate rapidly, emphasizing odor onset. Furthermore, weak ORN inputs are amplified in the PN layer but strong inputs are not. This nonlinear transformation broadens PN tuning and produces more uniform distances between odor representations in PN coding space. In addition, portions of the odor response profile of a PN are not systematically related to their direct ORN inputs, which probably indicates the presence of lateral connections between glomeruli. Finally, we show that a linear discriminator classifies odors more accurately using PN spike trains than using an equivalent number of ORN spike trains.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2838615 | PMC |
http://dx.doi.org/10.1038/nn1976 | DOI Listing |
Perspect Med Educ
December 2024
University of California, San Francisco, US.
When health professions learners do not meet standards on assessments, educators need to share this information with the learners and determine next steps to improve their performance. Those conversations can be difficult, and educators may lack confidence or skill in holding them. For clinician-educators with experience sharing challenging news with patients, using an analogy from clinical settings may help with these conversations in the education context.
View Article and Find Full Text PDFVaccine X
January 2025
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
Background: The innate immune response is important for the development of the specific adaptive immunity, however it may also be associated with reactogenicity after vaccination. We explore the association between innate responsiveness, reactogenicity, and antibody response after first COVID-19 vaccination.
Methods: We included 146 healthy Dutch individuals aged 12-59 who received their first BNT162b2 (Comirnaty, Pfizer) COVID-19 vaccination.
Sci Rep
December 2024
Department of Anatomy, Faculty of Science, Mahidol University, 272 Rama VI Road, Ratchathewi, Bangkok, 10400, Thailand.
SARS-CoV-2, the cause of COVID-19, primarily targets lung tissue, leading to pneumonia and lung injury. The spike protein of this virus binds to the common receptor on susceptible tissues and cells called the angiotensin-converting enzyme-2 (ACE2) of the angiotensin (ANG) system. In this study, we produced chimeric Macrobrachium rosenbergii nodavirus virus-like particles, presenting a short peptide ligand (ACE2tp), based on angiotensin-II (ANG II), on their outer surfaces to allow them to specifically bind to ACE2-overexpressing cells called ACE2tp-MrNV-VLPs.
View Article and Find Full Text PDFSci Rep
December 2024
School of Management Science and Engineering, Shandong Jianzhu University, Jinan, 250101, China.
This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Artificial Intelligence, Anhui University, Hefei, 230601, Anhui, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, Anhui, China; Anhui Provincial Engineering Research Center for Unmanned Systems and Intelligent Technology, Hefei, 230601, Anhui, China; School of Automation, Southeast University, Nanjing, 211189, Jiangsu, China. Electronic address:
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, more closely resemble the biological characteristics of efficient learning observed in the brain. In SNNs, spiking neurons exhibit complex dynamic characteristics and learn based on principles of biological plasticity.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!