Opioid Withdrawal for 4 Days Prevents Synaptic Depression Induced by Low Dose of Morphine or Naloxone in Rat Hippocampal CA1 Area In Vivo

The formation of memory is believed to depend on experience- or activity-dependent synaptic plasticity, which is exquisitely sensitive to psychological stress since inescapable stress impairs long-term potentiation (LTP) but facilitates long-term depression (LTD). Our recent studies demonstrated that 4 days of opioid withdrawal enables maximal extents of both hippocampal LTP and drug-reinforced behavior; while elevated-platform stress enables these phenomena at 18 h of opioid withdrawal. Here, we examined the effects of low dose of morphine (0.5 mg kg(-1), i.p.) or the opioid receptor antagonist naloxone (1 mg kg(-1), i.p.) on synaptic efficacy in the hippocampal CA1 region of anesthetized rats. A form of synaptic depression was induced by low dose of morphine or naloxone in rats after 18 h but not 4 days of opioid withdrawal. This synaptic depression was dependent on both N-methyl-D-aspartate receptor and synaptic activity, similar to the hippocampal long-term depression induced by low frequency stimulation. Elevated-platform stress given 2 h before experiment prevented the synaptic depression at 18 h of opioid withdrawal; in contrast, the glucocorticoid receptor (GR) antagonist RU38486 treatment (20 mg kg(-1), s.c., twice per day for first 3 days of withdrawal), or a high dose of morphine reexposure (15 mg kg(-1), s.c., 12 h before experiment), enabled the synaptic depression on 4 days of opioid withdrawal. This temporal shift of synaptic depression by stress or GR blockade supplements our previous findings of potentially correlated temporal shifts of LTP induction and drug-reinforced behavior during opioid withdrawal. Our results therefore support the idea that stress experience during opioid withdrawal may modify hippocampal synaptic plasticity and play important roles in drug-associated memory. (C) 2009 Wiley-Liss, Inc.

Disconnection of the hippocampal-prefrontal cortical circuits impairs spatial working memory performance in rats

There is a unidirectional, ipsilateral and monosynaptic projection from the hippocampus to the prefrontal cortex. The cognitive function of hippocampal-prefrontal cortical circuit is not well established. In this paper, we use muscimol treated rats to inv

Reversible disconnection of the hippocampal-prelimbic cortical circuit impairs spatial learning but not passive avoidance learning in rats

Wistar rats, treated with the GABA(A) receptor agonist muscimol, were used to investigate the role of the hippocampal-prelimbic cortical (Hip-PLC) circuit in spatial learning in the Morris water maze task, and in passive avoidance learning in the step-thr

Geometry property of multi-degree of freedom neurons

In this paper, we study a problem of geometric inequalities for a Multi-degree of Freedom Neurons. Some new geometric inequalities for a Multi-degree of Freedom Neurons are established. As special cases, some known inequalities are deduced.

Determining of the delay time for a heating ventilating and air-conditioning plant using multi-weights neurons approach

This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

The structure and function of neurons with variable nonlinear transfer function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

Face recognition based on PCA and multi-degree of freedom neurons

An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Development and transition of spiral wave in the coupled Hindmarsh-Rose neurons in two-dimensional space

The dynamics and the transition of spiral waves in the coupled Hindmarsh-Rose (H-R) neurons in two-dimensional space are investigated in the paper. It is found that the spiral wave can be induced and developed in the coupled HR neurons in two-dimensional space, with appropriate initial values and a parameter region given. However, the spiral wave could encounter instability when the intensity of the external current reaches a threshold value of 1.945. The transition of spiral wave is found to be affected by coupling intensity D and bifurcation parameter r. The spiral wave becomes sparse as the coupling intensity increases, while the spiral wave is eliminated and the whole neuronal system becomes homogeneous as the bifurcation parameter increases to a certain threshold value. Then the coupling action of the four sub-adjacent neurons, which is described by coupling coefficient D', is also considered, and it is found that the spiral wave begins to breakup due to the introduced coupling action from the sub-adjacent neurons (or sites) and together with the coupling action of the nearest-neighbour neurons, which is described by the coupling intensity D.

Primary culture and characteristic morphologies of medulla terminalis neurons in the eyestalks of Chinese shrimp, Fenneropenaeus chinensis

A method for culturing medulla terminalis (MT) neurons in the eyestalk of Chinese shrimp, Fenneropenaeus chinensis, was first established. The neurons showed immediate outgrowth in the culture medium supplemented with glutamine, glucose and antibiotics. The cells grew for about 2-7 days and then sustained for a week or more. At least six types of neurons were distinguished on the basis of size and form of soma and outgrowth pattern of cells. (C) 2003 Elsevier Science B.V. All rights reserved.