Sadeghnejad, N;
Ezoji, M;
Ebrahimpour, R;
Zabbah, S;
(2023)
Resolving the neural mechanism of core object recognition in space and time: A computational approach.
Neuroscience Research
, 190
pp. 36-50.
10.1016/j.neures.2022.12.002.
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Abstract
The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recognition are not potentially able to explain the recognition time and, thus, only focus on the recognition accuracy because of two reasons: lack of a temporal representation mechanism for sensory processing and using non-biological classifiers for decision-making processing. Here, we proposed a hierarchical temporal model of object recognition using a spiking deep neural network coupled to a biologically plausible decision-making model for explaining both recognition time and accuracy. We showed that the response dynamics of the proposed model can resemble those of the brain. Firstly, in an object recognition task, the model can mimic human's and monkey's recognition time as well as accuracy. Secondly, the model can replicate different speed-accuracy trade-off regimes as observed in the literature. More importantly, we demonstrated that temporal representation of different abstraction levels (superordinate, midlevel, and subordinate) in the proposed model matched the brain representation dynamics observed in previous studies. We conclude that the accumulation of spikes, generated by a hierarchical feedforward spiking structure, to reach abound can well explain not even the dynamics of making a decision, but also the representations dynamics for different abstraction levels.
| Type: | Article |
|---|---|
| Title: | Resolving the neural mechanism of core object recognition in space and time: A computational approach |
| Location: | Ireland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neures.2022.12.002 |
| Publisher version: | https://doi.org/10.1016/j.neures.2022.12.002 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Accumulation to bound model, Deep spiking convolutional neural network, Dynamical representational dissimilarity matrix, Speed- accuracy Trade-off, Temporal object recognition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10165396 |
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