794 citations · 1.6k across the 12 of their papers we have counts for
12 papers
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning
Alireza Nadafian, Milad Mozafari, Timothée Masquelier +1
The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computat…
Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP
Arsham Gholamzadeh Khoee, Alireza Javaheri, Saeed Reza Kheradpisheh +1
The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning…
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks
Fatemeh Sharifizadeh, Mohammad Ganjtabesh, Abbas Nowzari-Dalini
The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels. During the l…
Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks
Alireza Nadafian, Mohammad Ganjtabesh
The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an o…
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +1
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and en…
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini +2
The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less en…
First-spike based visual categorization using reward-modulated STDP
Milad Mozafari, Saeed Reza Kheradpisheh, Timothée Masquelier +2
Reinforcement learning (RL) has recently regained popularity, with major achievements such as beating the European game of Go champion. Here, for the first time, we show that RL ca…
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer
Matin N. Ashtiani, Saeed Reza Kheradpisheh, Timothée Masquelier +1
The human visual system contains a hierarchical sequence of modules that take part in visual perception at different levels of abstraction, i.e., superordinate, basic, and subordin…
STDP-based spiking deep convolutional neural networks for object recognition
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J Thorpe +1
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity…
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh +1
View-invariant object recognition is a challenging problem, which has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notor…
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh +1
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases.…
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Timothée Masquelier
Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and nonrigid defo…