1.4k citations · 3.1k across the 22 of their papers we have counts for
23 papers
Forward-Only Convolutional Neural Networks with Learnable Channel-Class Assignment
Mohammadnavid Ghader, Saeed Reza Kheradpisheh, Bahar Farahani +1
The Forward-Forward (FF) algorithm offers a biologically inspired alternative to backpropagation by replacing gradient-based credit assignment with local, forward-only objectives.…
A Multiplication-Free Spike-Time Learning Algorithm and its Efficient FPGA Implementation for On-Chip SNN Training
Maryam Mirsadeghi, Mojtaba Mirbagheri, Saeed Reza Kheradpisheh
Spiking Neural Networks (SNNs) offer a biologically inspired foundation for low-power, event-driven intelligence, yet their direct on-chip supervised training remains a key hardwar…
Persian-Phi: Efficient Cross-Lingual Adaptation of Compact LLMs via Curriculum Learning
Amir Mohammad Akhlaghi, Amirhossein Shabani, Mostafa Abdolmaleki +1
The democratization of AI is currently hindered by the immense computational costs required to train Large Language Models (LLMs) for low-resource languages. This paper presents Pe…
Detecting Cadastral Boundary from Satellite Images Using U-Net model
Neda Rahimpour Anaraki, Maryam Tahmasbi, Saeed Reza Kheradpisheh
Finding the cadastral boundaries of farmlands is a crucial concern for land administration. Therefore, using deep learning methods to expedite and simplify the extraction of cadast…
Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
Mohammadnavid Ghader, Saeed Reza Kheradpisheh, Bahar Farahani +1
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantage…
Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
Mahdi Movahedian Moghaddam, Kourosh Parand, Saeed Reza Kheradpisheh
In this paper, we present the Residual Integral Solver Network (RISN), a novel neural network architecture designed to solve a wide range of integral and integro-differential equat…
Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN
Neda Rahimpour Anaraki, Alireza Azadbakht, Maryam Tahmasbi +3
Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods…
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…
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Alireza Azadbakht, Saeed Reza Kheradpisheh, Ismail Khalfaoui-Hassani +1
Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segment…
Spiking neural networks trained via proxy
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN networks, respect…
BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks
Hafez Ghaemi, Erfan Mirzaei, Mahbod Nouri +1
Brain-inspired computation and information processing alongside compatibility with neuromorphic hardware have made spiking neural networks (SNN) a promising method for solving lear…
Spike time displacement based error backpropagation in convolutional spiking neural networks
Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh +1
We recently proposed the STiDi-BP algorithm, which avoids backward recursive gradient computation, for training multi-layer spiking neural networks (SNNs) with single-spike-based t…
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and…
Action Recognition Using Supervised Spiking Neural Networks
Aref Moqadam Mehr, Saeed Reza Kheradpisheh, Hadi Farahani
Biological neurons use spikes to process and learn temporally dynamic inputs in an energy and computationally efficient way. However, applying the state-of-the-art gradient-based s…
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
Saeed Reza Kheradpisheh, Timothée Masquelier
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all…
Deep Learning in Spiking Neural Networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh +2
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural ne…
Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection
Timothée Masquelier, Saeed Reza Kheradpisheh
Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding),…
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…