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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…
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…
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…
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),…