Computational or in silico Modeling is a subset of systems biology that is concerned with integrating large datasets using complex data structures and algorithms to accurately recreate the behaviour of a system. It is used to determine how the internal components of complex biological systems interact with each other and with external systems. Computational Modeling approaches have been applied to a number of biological systems at a variety of spatial and temporal scales to complement and enhance experimental research. For example Computational Modeling approaches are currently used in design and development of the next generation of cardiac pacemakers. Despite the attractive prospects of applying Computational Modeling systems in understanding complex events leading to conception and development of embryo, the application of in silico Modeling to reproductive medicine is still in its infancy. In recent years we have tried to advance development of an in silico model of gamete interaction with female reproductive tract in our laboratory by creating a 3-D model of oviduct. Integration of this 3-D model with simple sperm movements has created a relatively simple in silico model that can help us to understand sperm transport in the female reproductive tract. We hope progression in this field will allow us to create simulations that are more complex and incorporate different factors in their predictions of sperm movements. These simulations will help us in understanding sperm transport within female reproductive tract and will be the first steps in reaching the ultimate goal of this project that is to predict the milieu of reproductive tract at the time of conception.

Optogenetics is an advanced optical tool in neuroscience research. However, light stimulation in optogenetic experiments may also affect neural function by generating heat. In this paper, the effect of increasing the temperature of the brain tissue was studied during light stimulation. The Hodgkin-Huxley model and the hippocampal pyramidal cell model have been used to investigate the effect of temperature on spike neurons. The Modeling results show that irradiation of brain tissue by pulsed laser with a frequency of 40 Hz, the duty cycle of 90% and wavelength of 593 nm at a distance of 10 μ, m from the tip of the fiber, for 60 seconds with a power of 1 and 40 mW leads to the temperature change from 37 °, C to 39 °, C. The obtained results show that the laser intensity decreases to zero at a distance of 1 mm from the tip of a fiber, which is absorbed by the tissue and causes a temperature rise of 2 °, C that can increase the spike rate of neurons by 16. 6%.

In this research connectionist Modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist Modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decision making the role of Amygdala is so important and we expect that a model with two parts (thalamus and Amygdala) can have the best result in Modeling participants decisions without considering any part for cortex process. For this purpose 56 participants composed of 20 men and 36 women wanted to do Iowa Gambling Task. Results show that the networks related to two parts model predict 62. 57 Percent’ s of participant’ s decisions and the 3parts model has 68. 46 Percent’ s of that. In conclusion it can be said that three parts Modeling has been more success than mathematical two parts model in predicting the performance of participants and the difference is significant. In other words cortex role in this kind of decision making is quite important.

Introduction: This study investigates the Computational mechanisms underlying binocular disparity discrimination in visual area V4 through a novel Modeling approach that examines the ability of V4 neurons to discriminate between fine disparities near the fixation plane and develops a comprehensive Computational framework to characterize this process. This study’s central hypothesis posits that disparity discrimination ability in V4 depends on two key factors: The absolute difference between disparity pairs and their proximity to zero disparity. Methods: The present study analyzed electrophysiological data from 156 V4 neurons recorded from macaque monkeys during presentation of random-dot stereograms with varying binocular disparities (±1.2°, ±0.6°, ±0.3°, 0°) and correlation levels. A novel population-level metric, the Disparity Discrimination Ability Index (DDAI), was introduced to quantify the neural population’s capacity to distinguish between disparity pairs using receiver operating characteristic-based (ROC-based) analysis. The DDAI was computed as the average normalized area under the ROC curve across all neurons for each stimulus pair. Results: This study’s results confirmed that V4 neurons exhibit specialized tuning for near-zero disparities, with response variability (coefficient of variation) being minimal for stimuli close to zero disparity and increasing with absolute disparity magnitude. The Computational model DCM (DDAI Computational Model) successfully predicted experimental DDAI patterns with high accuracy (Pearson r=0.969, Spearman P=0.887). Statistical equivalence testing (TOST) confirmed that model predictions were practically equivalent to experimental data (mean difference=0.00018, 90% CI within ±0.03). Conclusion: The obtained findings demonstrate that V4 employs an efficient coding strategy prioritizing precision near the fixation depth while maintaining coarser encoding for larger disparities. The DCM framework provides a robust foundation for understanding population-level disparity processing and offers potential applications in artificial vision systems and clinical assessment of stereoscopic vision disorders.