Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Alireza Alemi"'
Publikováno v:
PLoS Computational Biology, Vol 11, Iss 8, p e1004439 (2015)
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, wh
Externí odkaz:
https://doaj.org/article/3f1adf5ca18a47ad86d6f2a4bd89ae2a
Publikováno v:
Institution of Mechanical Engineers. Proceedings. Part F: Journal of Rail and Rapid Transit, 234 (2020)(9)
Proceedings of the Institution of Mechanical Engineers. Part F, Journal of Rail and Rapid Transit, 234 (9)
Proceedings of the Institution of Mechanical Engineers. Part F, Journal of Rail and Rapid Transit, 234 (9)
A wheel impact load detector is used to assess the condition of a railway wheel by measuring the dynamic forces generated by defects. This system normally measures the impact force at multiple points by exploiting multiple sensors to collect samples
Autor:
Carlo Baldassi, Alireza Alemi-Neissi, Marino Pagan, James J Dicarlo, Riccardo Zecchina, Davide Zoccolan
Publikováno v:
PLoS Computational Biology, Vol 9, Iss 8, p e1003167 (2013)
The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recen
Externí odkaz:
https://doaj.org/article/359d654a9d32401f80700e07e74ac07d
Publikováno v:
Proceedings of the Institution of Mechanical Engineers. Part F, Journal of Rail and Rapid Transit
Institution of Mechanical Engineers. Proceedings. Part F: Journal of Rail and Rapid Transit, 233(1)
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 233 (1)
Institution of Mechanical Engineers. Proceedings. Part F: Journal of Rail and Rapid Transit, 233(1)
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 233 (1)
Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The
Publikováno v:
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 231:961-981
Condition monitoring systems are commonly exploited to assess the health status of equipment. A fundamental part of any condition monitoring system is data acquisition. Meaningfully estimating the current condition and predicting the future behaviour
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
The brain uses spikes in neural circuits to perform many dynamical computations. The computations are performed with properties such as spiking efficiency, i.e. minimal number of spikes, and robustness to noise. A major obstacle for learning computat
Publikováno v:
Structural Health Monitoring 2017.
Wheel Impact Load Detectors are common devices that measure the rail response made by the wheel-rail contact to estimate the condition of the in-service wheels. The data collected by the multiple sensors can be fused to reconstruct a wheel-rail conta
Understanding how the brain learns to compute functions reliably, efficiently and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddb4fe4722de308a008a6a34858df0d9
Autor:
Alireza Alemi, Amir Mohammadipour
Publikováno v:
Journal of biomechanics. 65
Computational models are important tools which help researchers understand traumatic brain injury (TBI). A mechanistic multi-scale numerical approach is introduced to quantify diffuse axonal injury (DAI), the most important mechanism of TBI, induced
Autor:
Alireza Alemi
Publikováno v:
Alireza Alemi
Attractor neural network is an important theoretical scenario for modeling memory function in the hippocampus and in the cortex. In these models, memories are stored in the plastic recurrent connections of neural populations in the form of "attractor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68333c32242c3a89c1c2464616cfa17b
http://arxiv.org/abs/1512.01213
http://arxiv.org/abs/1512.01213