There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topographical differences are quite distinguishable. In our proposed model, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.

We spend one third of our life in sleep. The interesting point about the sleep is that the neurons are not quiescent during sleeping and they show synchronous oscillations at different regions. Especially sharp wave ripples are observed in the hippocampus. Here, we propose a simple phenomenological Neural mass model for the CA1-CA3 network of the hippocampus considering the spike frequency adaptation for excitatory neurons. The model consists of one group of identical CA1 excitatory neurons, one group of identical CA1 inhibitory neurons, one group of identical CA3 excitatory neurons, and one group of identical CA3 inhibitory neurons. All the recurrent connections between the neurons of CA3 network are considered. For CA1 neurons the excitatory to inhibitory, inhibitory to excitatory and inhibitory to inhibitory connections are considered. CA1 and CA3 neurons are connected by long-range connections from CA3 excitatory neurons to both CA1 excitatory and inhibitory neurons. We show that this simple model can spontaneously generate the oscillations similar to the sharp waves in the CA3 network. The duration of the sharp waves is determined by the slow dynamic of the adaptation process. The excitatory inputs from CA3 network to the CA1 network during these sharp waves induce ripples in the CA1 network due to the interaction of excitatory and inhibitory neurons. We next show that contrary to intuition and in a very good agreement with the recent experimental findings, reduction of the excitation increases the amplitude of the ripples while decreases the frequency of them. This model can also spontaneously generate ripple doublets. The decrease in the excitation is associated with the increase in the probability of observing ripple doublets. Our results shed light on our understanding of the mechanism underlying the generation of sharp wave ripples.

In this work, multilayer perceptron network are used to predict the mass transfer flux of CO2 in Piperazine solution. The effective parameters on the absorption flux of CO2 such as interfacial and bulk concentration, CO2 loading, ratio of diffusion coefficient of gas to liquid of CO2, ratio of the CO2 partial pressure to the total pressure, ratio of film thickness of gas to liquid and the film parameter as input variables and mass transfer flux of CO2 as output variables were selected. Experimental data presented in the literature were used for training and evaluating the multilayer Neural network of Perceptron. A total of 104 experimental data were used and total concentrations of piperazine were 2-8 mol/lit. The predicting results of Neural network indicated that the mean square error for mass transfer flux was 8.61%. In addition, the results of Neural network were compared with the predictions of other researchers and the findings revealed that the artificial Neural network computes the mass transfer flux of CO2 more accurately and quickly.

Background & Objectives: Cardiovascular events are among the most common causes of mortality in patients with end stage renal disease. Caediac biomarkers such as troponins are very sensitive in diagnosing heart problems. Troponin I can be used to diagnose acute cardiovascular problems in hemodialysis patients.Methods: In -this descriptive-analytical and cross-sectional study the level of troponin I was measured in 39 hemodialysis patients using ELISA method and BUN, Cr and Hb level before hemodialysis. LVH and LVMI were determined by echocardiography. The data were analyzed using SPSS.Results: The patients were 52.92 years old on average. 26 patients were male and 13 female.The average of tropnin I was 0.78 µg/l. There was no meaningful relationship between troponin I and LVMI, age and sex. However, a significant relationship was found between the level of troponin I with EF and diastolic dysfunction (p=0.05).Conclusion: Troponin I can be regarded as an indicator of LV dysfunction.

In the present research, Neural networks were applied to predict mass transfer flux of CO2 in aqueous amine solutions. Buckingham π theorem was used to determine the effective dimensionless parameters on CO2 mass transfer flux in reactive separation processes. The dimensionless parameters including CO2 loading, the ratio of CO2 diffusion coefficient of gas to a liquid, the ratio of the CO2 partial pressure to the total pressure, the ratio of film thickness of gas to liquid and film parameter as input variables and mass transfer flux of CO2 as output variables were in the modeling. A multilayer perceptron network was used in the prediction of CO2 mass transfer flux. As a case study, experimental data of CO2 absorption into Piperazine solutions were used in the learning, testing, and evaluating steps of the multilayer perceptron. The optimal structure of the multilayer perceptron contains 21 and 17 neurons in two hidden layers. The predicting results of the network indicated that the mean square error for mass transfer flux was 4. 48%. In addition, the results of the multilayer perceptron were compared with the predictions of other researchers’ results. The findings revealed that the artificial Neural network computes the mass transfer flux of CO2 more accurately and more quickly.