Masoud Ghodrati
Affiliations: | Physiology | Monash University, Caulfield East, Victoria, Australia |
Area:
Visual systemGoogle:
"Masoud Ghodrati"Mean distance: 15.98 (cluster 17) | S | N | B | C | P |
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Publications
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| Hadjidimitrakis K, Ghodrati M, Breveglieri R, et al. (2020) Neural coding of action in three dimensions: Task- and time-invariant reference frames for visuospatial and motor-related activity in parietal area V6A. The Journal of Comparative Neurology |
| Ramezani F, Kheradpisheh SR, Thorpe SJ, et al. (2019) Object categorization in visual periphery is modulated by delayed foveal noise. Journal of Vision. 19: 1 |
| Ghodrati M, Zavitz E, Rosa MGP, et al. (2019) Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation. Nature Communications. 10: 941 |
| Tavanaei A, Ghodrati M, Kheradpisheh SR, et al. (2019) Deep learning in spiking neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 111: 47-63 |
| Ghodrati M, Khaligh-Razavi SM, Lehky SR. (2017) Towards building a more complex view of the lateral geniculate nucleus: recent advances in understanding its role. Progress in Neurobiology |
| Ghodrati M, Ghodousi M, Yoonessi A. (2016) Low-Level Contrast Statistics of Natural Images Can Modulate the Frequency of Event-Related Potentials (ERP) in Humans. Frontiers in Human Neuroscience. 10: 630 |
| Kheradpisheh SR, Ghodrati M, Ganjtabesh M, et al. (2016) Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder. Frontiers in Computational Neuroscience. 10: 92 |
| Kheradpisheh SR, Ghodrati M, Ganjtabesh M, et al. (2016) Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition. Scientific Reports. 6: 32672 |
| Ghodrati M, Alwis DS, Price NS. (2016) Orientation selectivity in rat primary visual cortex emerges earlier with low contrast and high luminance stimuli. The European Journal of Neuroscience |
| Farzmahdi A, Rajaei K, Ghodrati M, et al. (2016) A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans. Scientific Reports. 6: 25025 |