A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System

Karimi, Mohammad; Ebrahimpour, Reza; Bagheri, Nasour

doi:10.1155/2021/8895579

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Thus, the second experiment, which was the important aspect of the present study in comparison to the other models of object recognition [10,11,24,35], provided stronger evidence about the brain plausibility of temporal representation in the proposed model. Thus, we believe that the model is a good candidate for delving deeper into the underlying mechanisms of object recognition in the brain through computational modeling [69].…”

Section: Discussionmentioning

confidence: 99%

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task

Sadeghnejad,

Ezoji,

Ebrahimpour

et al. 2024

J. Neural Eng.

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Objective. Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision. Approach. Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model. Main results. The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10−173) and the time of the peak response (p-value < 10−31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10−59) and accuracy (p-value < 10−165)). Significance. We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r = 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

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