In general, strength of input to neocortical neural circuits affects the amplitude of postsynaptic potentials (PSPs), thereby modulating the way signals are transmitted within the circuits. Caffeine is one of the pharmacological agents able to modulate synaptic activities. The present study investigated how strength of input affects signal propagation in neocortical circuits under the application of caffeine. Spatio-temporal neural activities were observed from visual cortical slices of rats using optical recording methods with voltage-sensitive dye. Electrical stimulations were applied to white matter in the primary visual cortex with bath-application of caffeine. When the strength of stimulation was 0.3mA, signals propagated from the site of stimulation in the primary visual cortex toward the secondary visual cortex along vertical and horizontal pathways. When stimulation strength was reduced from 0.3mA to 0.07mA, start of signal propagation was delayed about 25ms without affecting field PSP amplitude or the manner of signal propagation. Conversely, co-application of caffeine and d-2-amino-5-phosphonovaleric acid (d-AP5) did not induce delays in signal start. These findings suggest that conversion of neural code from amplitude code to temporal code is inducible at the level of neocortical circuits in an N-methyl-d-aspartate (NMDA) receptor activity-dependent manner.
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http://dx.doi.org/10.1016/j.neures.2011.05.010 | DOI Listing |
Neuropharmacology
January 2025
Department of Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, 78229, USA. Electronic address:
Kappa opioid receptors (KOR) expressed by peripheral pain-sensing neurons (nociceptors) are a promising target for development of effective and safer analgesics for inflammatory pain that are devoid of central nervous system adverse effects. Here we sought to delineate the signaling pathways that underlie peripheral KOR-mediated antinociception in adult male and female Sprague-Dawley rats. In an inflammatory model of pain, local intraplantar (i.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
View Article and Find Full Text PDFInterest in carbon dioxide (CO) sensors is growing rapidly due to the increasing awareness of the link between air quality and health. Indoor, high CO levels signal poor ventilation, and outdoor the burning of fossil fuels and its associated pollution. CO gas sensors based on integrated optical waveguides are a promising solution due to their excellent gas sensing selectivity, compact size, and potential for mass manufacturing large volumes at low cost.
View Article and Find Full Text PDFOptical nonreciprocal devices are critical components in integrated photonic systems and scalable quantum technologies. We propose an all-optical approach to achieve integrated optical nonreciprocity utilizing a moving index grating. The grating is generated in a nonlinear optical waveguide through the Kerr effect by driving the waveguide with two counter-propagating pump fields of slightly different frequencies.
View Article and Find Full Text PDFFor indirect time-of-flight (iToF) cameras, we proposed a modeling approach focused on addressing random error. Our model characterizes random error comprehensively by detailing the propagation of error introduced by signal light, ambient light, and dark noise through phase calculation and system correction processes. This framework leverages correlations between incident light and tap responses to quantify noise impacts accurately.
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