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Abbas Nowzari-Dalini
Researcher at University of Tehran
Publications - 58
Citations - 978
Abbas Nowzari-Dalini is an academic researcher from University of Tehran. The author has contributed to research in topics: Time complexity & Spiking neural network. The author has an hindex of 14, co-authored 58 publications receiving 823 citations.
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Kavosh: a new algorithm for finding network motifs
Zahra Razaghi Moghadam Kashani,Hayedeh Ahrabian,Elahe Elahi,Abbas Nowzari-Dalini,Elnaz Saberi Ansari,Sahar Asadi,Shahin Mohammadi,Falk Schreiber,Falk Schreiber,Ali Masoudi-Nejad +9 more
TL;DR: A new algorithm, Kavosh, for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms, based on counting all k- size sub-graphs of a given graph (directed or undirected).
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First-Spike-Based Visual Categorization Using Reward-Modulated STDP
Milad Mozafari,Saeed Reza Kheradpisheh,Timothée Masquelier,Abbas Nowzari-Dalini,Mohammad Ganjtabesh +4 more
TL;DR: For the first time, it is shown that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier.
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Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
TL;DR: In this article, a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme were used to address the limitations of deep artificial neural networks, which have revolutionized the computer vision domain.
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Reconstruction of an Integrated Genome-Scale Co-Expression Network Reveals Key Modules Involved in Lung Adenocarcinoma
Gholamreza Bidkhori,Zahra Narimani,Saman Hosseini Ashtiani,Ali Moeini,Abbas Nowzari-Dalini,Ali Masoudi-Nejad +5 more
TL;DR: A number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules and having an over-expression pattern similar to that of EGFR.
Journal ArticleDOI
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron.
TL;DR: SpykeTorch as discussed by the authors is an open-source high-speed simulation framework based on PyTorch, which simulates convolutional SNNs with at most one spike per neuron and the rank-order encoding scheme.