The role of dopamine receptors in regulating the formation of recognition memory remains poorly understood. Here we show the effects of systemic administration of dopamine receptor agonists and antagonists on the formation of memory for novel object recognition in rats. In Experiment I, rats received an intraperitoneal (i.p.) injection of vehicle, the selective D1 receptor agonist SKF38393 (1.0 and 5.0mg/kg), or the D2 receptor agonist quinpirole (1.0 and 5.0mg/kg) immediately after training. In Experiment II, rats received an injection of vehicle, the dopamine receptor antagonist SCH23390 (0.1 and 0.05 mg/kg), or the D2 receptor antagonist raclopride (0.5 and 0.1mg/kg) before training, followed by an injection of vehicle or the nonselective dopamine receptor agonist apomorphine (0.05 mg/kg) immediately after training. SKF38393 at 5mg/kg produced an enhancement of novel object recognition memory measured at both 24 and 72 h after training, whereas the dose of 10mg/kg impaired 24-h retention. Posttraining administration of quinpirole did not affect 24-h retention. Apomorphine enhanced memory in rats given pretraining raclopride, suggesting that the effect was mediated by selective activation of D1 receptors. The results indicate that activation of D1 receptors can enhance recognition memory consolidation. Importantly, pharmacological activation of D1 receptors enhanced novel object recognition memory even under conditions in which control rats showed significant retention.
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http://dx.doi.org/10.1016/j.nlm.2010.12.007 | DOI Listing |
J Integr Neurosci
January 2025
Department of Psychology, The Affiliated Hospital of Jiangnan University, 214151 Wuxi, Jiangsu, China.
Background: Deficits in emotion recognition have been shown to be closely related to social-cognitive functioning in schizophrenic. This study aimed to investigate the event-related potential (ERP) characteristics of social perception in schizophrenia patients and to explore the neural mechanisms underlying these abnormal cognitive processes related to social perception.
Methods: Participants included 33 schizophrenia patients and 35 healthy controls (HCs).
Sensors (Basel)
January 2025
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.
The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states.
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January 2025
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.
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January 2025
Department of Artifcial Intelligence, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea.
Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.
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January 2025
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers' and students' classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines.
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