A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition

Abstract Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. Howeve...

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Autores principales:	Hamed Heidari-Gorji, Reza Ebrahimpour, Sajjad Zabbah
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f919e0c6616b4a52abb110709f665705
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spelling	oai:doaj.org-article:f919e0c6616b4a52abb110709f6657052021-12-02T11:37:23ZA temporal hierarchical feedforward model explains both the time and the accuracy of object recognition10.1038/s41598-021-85198-22045-2322https://doaj.org/article/f919e0c6616b4a52abb110709f6657052021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85198-2https://doaj.org/toc/2045-2322Abstract Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.Hamed Heidari-GorjiReza EbrahimpourSajjad ZabbahNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hamed Heidari-Gorji Reza Ebrahimpour Sajjad Zabbah A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
description	Abstract Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.
format	article
author	Hamed Heidari-Gorji Reza Ebrahimpour Sajjad Zabbah
author_facet	Hamed Heidari-Gorji Reza Ebrahimpour Sajjad Zabbah
author_sort	Hamed Heidari-Gorji
title	A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
title_short	A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
title_full	A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
title_fullStr	A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
title_full_unstemmed	A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
title_sort	temporal hierarchical feedforward model explains both the time and the accuracy of object recognition
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f919e0c6616b4a52abb110709f665705
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A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition

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