Computational Neuroscience
Maryam Moghadam,; Farzad Towhidkhah; Golnaz Baghdadi
Volume 15, Issue 2 , August 2021, , Pages 111-125
Abstract
In cognition physiology and neuroscience, spatial memory is responsible for the maintenance and recall of information related to environmental details, orientation, and spatial navigation. The brain’s cognitive functions including navigation are executed through correlated and sequential activities ...
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In cognition physiology and neuroscience, spatial memory is responsible for the maintenance and recall of information related to environmental details, orientation, and spatial navigation. The brain’s cognitive functions including navigation are executed through correlated and sequential activities of different regions. According to previous research, navigation is largely related to the activities of the Hippocampus (HPC) and the Medial Temporal Lobe (MTL), and retrieval of spatial memories from these regions is controlled by the frontal region and specifically medial prefrontal cortex (mPFC). In this paper we attempt to provide a navigation cognitive model based on computational concepts focusing on bidirectional interaction between HPC and mPFC. This model is provided considering 1. The lack of a comprehensive cognitive model of navigation on a previously learned path and ambiguities regarding the information transferring between the regions, and 2. Disagreement between available models and the currently known actual information flow occurring within the brain. The model is inclusive of the active brain regions engaged in navigation using the cognitive map. Furthermore, we propose a computational model based on van-der-pol neuron pools and controlling rule-base, which is naturally related to the actual brain activity through the synchrony mechanism for information transfer and the mPFC rule-based control of the medial temporal lobe. Finally, by analyzing and presenting evidence, we have shown that the model can be beneficial and practical for describing cognitive and functional disorders in navigation, also for design and prediction of the outcomes of therapeutic and rehabilitation protocols in diseases related to spatial navigation, such as the Alzheimer’s disease.
Computational Neuroscience
Maryam Sadeghi Talarposhti; Mohammad Ali Ahmadi-Pajouh; Frazad Towhidkhah
Volume 14, Issue 4 , February 2021, , Pages 333-344
Abstract
Human being is capable of performing more than one task simultaneously. This ability has been investigated in many researches. Performing more than one task at the same time has always been a challenging topic in psychology and human perception fields. The output and the effect of two tasks have been ...
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Human being is capable of performing more than one task simultaneously. This ability has been investigated in many researches. Performing more than one task at the same time has always been a challenging topic in psychology and human perception fields. The output and the effect of two tasks have been studied in previous researches for understanding the brain’s performance and also the disease origin and the symptoms. The influence of different difficulty levels has been explored via discrete-continuous motor-cognitive dual-task (DT). To this aim, a manual tracking task combined with discrete auditory stimuli to establish DT procedure. Twenty-five participants in this paradigm were asked to track the target on screen while reacting to the auditory task at the same time. Two levels of difficulty in manual tracking plus a single auditory task (ST) were considered for the experiment. The variability of output via different difficulties was investigated by analyzing factors of error rate and the response time (RT). For this analysis, a Drift Diffusion Model (DDM) method was used. In this 4-parameter model, the drift parameter is assumed to show the difficulty levels. The results show that by applying different drift rates (the average of 0.5, 0.3, and 0.2), the model is consistent with experimental output RT and the drift factor has the potential to be considered as the difficulty factor in the DT procedure.
Computational Neuroscience
Naser Sadeghnejad; Mehdi Ezoji; Reza Ebrahimpour
Volume 14, Issue 1 , May 2020, , Pages 69-79
Abstract
Object recognition is one of the main cognitive abilities of human and animals. Human visual system, as a fast and accurate system can be a source of inspiration for the computational models of object recognition. Studies on the human visual system have emphasized its processing over time, whereas it ...
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Object recognition is one of the main cognitive abilities of human and animals. Human visual system, as a fast and accurate system can be a source of inspiration for the computational models of object recognition. Studies on the human visual system have emphasized its processing over time, whereas it is not considered in the conventional computational models of object recognition. In this paper, we attempt to present a time-based multilevel model for object recognition. In the first layer of the model, the input image information is sent to the next layer in a temporal representation. In the middle layer of the model, a deep neural network is used as a feature extractor. Finally, in contrast to the popular computational models for object recognition, a decision-making model such as drift-diffusion model is proposed based on the neuronal decision-making mechanisms in the brain. In other words, adaption to the human visual system has been considered in all of three layers. Several experiments have been conducted to evaluate the performance of the proposed computational model in object recognition. The experimental results show that as the input image becomes more complicated, noise increases, or occlusion occurs, the performance/reaction time of the model decreases/increases, which is consistent with the behavior of human visual system. The performance of the model for object recognition and base-level categorization is also investigated for application of the original images and the inverted images. The results show the difference between the processes of the object recognition and base-level categorization, which is consistent with the behavior of human visual system reported in the referenced papers.
