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.
Cell Biomechanics / Cell Mechanics / Mechanobiology
sajad ghazavi; Bahman Vahidi
Volume 10, Issue 3 , October 2016, , Pages 257-266
Abstract
Due to the importance of the brain and neurons, a vast area of research has been conducted in this field. However, due to the complexity of the neural behavior, each study investigated the functionality of neurons from one perspective such as electrophysiological, chemical, or mechanical perspective. ...
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Due to the importance of the brain and neurons, a vast area of research has been conducted in this field. However, due to the complexity of the neural behavior, each study investigated the functionality of neurons from one perspective such as electrophysiological, chemical, or mechanical perspective. In spite of the large number of research conducted on the brain injury topic, there is no study investigating the interaction of the mechanical and electrical characteristics of the neurons and its effect on the cell functionality. Understating the interaction between the mechanical and electrical properties of a neuron will have a substantial effect on treating neurological diseases such as traumatic brain injury and improving treatment methods such as ultrasound. As a result, there is a vital need to simulate the effect of mechanical forces on the electrophysiological behavior of a neuron. This study is one of the few attempts to achieve this goal by taking into account the mechanosensitivity of ion channels which affects the action potentials. Our proposed comprehensive model is based on power law equation (fractional dashpot) for mechanical modeling, Hodgkin Huxley (HH) equation for electrophysiological model and recent experiments for combination of these two equations. Based on the model, the calculated strain from the power law equation affects the activation and inactivation of ion channels. By changing the activation and inactivation variable in the HH equation, we can evaluate the effect of strain and mechanical stimulation on neural function. The results reveal neuron functions’ deficiency during neuron mechanical damage. As a result, action potential signal’s amplitude reduces. This reduction in amplitude of the action potential may be reversible or irreversible based on the amount of damage (plastic deformation).
