Prevention of social stress-escalated cocaine self-administration by CRF-R1 antagonist in the rat VTA

Intermittent exposure to social defeat stress can induce long-term neural plasticity that may influence escalated cocaine-taking behavior. Stressful encounters can lead to activation of dopamine neurons in the ventral tegmental area (VTA), which are modulated by corticotropin releasing factor (CRF) neurons.The study aims to prevent the effects of intermittently scheduled, brief social defeat stress on subsequent intravenous (IV) cocaine self-administration by pretreatment with a CRF receptor subtype 1 (CRF-R1) antagonist.Long-Evans rats were submitted to four intermittent social defeat experiences separated by 72 h over 10 days. Two experiments examined systemic or intra-VTA antagonism of CRF-R1 subtype during stress on the later expression of locomotor sensitization and cocaine self-administration during fixed (0.75 mg/kg/infusion) and progressive ratio schedules of reinforcement (0.3 mg/kg/infusion), including a continuous 24-h "binge" (0.3 mg/kg/infusion).Pretreatment with a CRF-R1 antagonist, CP 154,526, (20 mg/kg i.p.) prior to each social defeat episode prevented the development of stress-induced locomotor sensitization to a cocaine challenge and prevented escalated cocaine self-administration during a 24-h "binge". In addition, pretreatment with a CRF-R1 antagonist (0.3 mu g/0.5 mu l/side) into the VTA prior to each social defeat episode prevented stress-induced locomotor sensitization to a cocaine challenge and prevented escalated cocaine self-administration during a 24-h "binge".The current results suggest that CRF-R1 subtype in the VTA is critically involved in the development of stress-induced locomotor sensitization which may contribute to escalated cocaine self-administration during continuous access in a 24-h "binge".

Role of the bed nucleus of the stria terminalis in cardiovascular changes following chronic treatment with cocaine and testosterone: A role beyond drug seeking in addiction?

Neural plasticity has been observed in the bed nucleus of the stria terminalis (BNST) following exposure to both cocaine and androgenic-anabolic steroids. Here we investigated the involvement of the BNST on changes in cardiovascular function and baroreflex activity following either single or combined administration of cocaine and testosterone for 10 consecutive days in rats. Single administration of testosterone increased values of arterial pressure, evoked rest bradycardia and reduced baroreflex-mediated bradycardia. These effects of testosterone were not affected by BNST inactivation caused by local bilateral microinjections of the nonselective synaptic blocker CoCl2. The single administration of cocaine as well as the combined treatment with testosterone and cocaine increased both bradycardiac and tachycardiac responses of the baroreflex. Cocaine-evoked baroreflex changes were totally reversed after BNST inactivation. However, BNST inhibition in animals subjected to combined treatment with cocaine and testosterone reversed only the increase in reflex tachycardia, whereas facilitation of reflex bradycardia was not affected by local BNST treatment with CoCl2. In conclusion, the present study provides the first direct evidence that the BNST play a role in cardiovascular changes associated with drug abuse. Our findings suggest that alterations in cardiovascular function following subchronic exposure to cocaine are mediated by neural plasticity in the BNST. The single treatment with cocaine and the combined administration of testosterone and cocaine had similar effects on baroreflex activity, however the association with testosterone inhibited cocaine-induced changes in the BNST control of reflex bradycardia. Testosterone-induced cardiovascular changes seem to be independent of the BNST. © 2013 IBRO.

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (c) 2005 Elsevier B.V. All rights reserved.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Análise da estabilidade estática de tensão de sistemas elétricos de potência usando uma rede neural baseada na teoria da ressonância adaptativa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de carga multinodal usando a rede neural ART-ARTMAP fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Sexual dimorphism and phenotypic plasticity in the antennal lobe of a stingless bee, Melipona scutellaris

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)