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    Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches

    , Article Genomics ; Volume 102, Issue 4 , October , 2013 , Pages 195-201 ; 08887543 (ISSN) Nassiri, I ; Masoudi Nejad, A ; Jalili, M ; Moeini, A ; Sharif University of Technology
    2013
    Abstract
    A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises... 

    ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices

    , Article IEEE Transactions on Biomedical Circuits and Systems ; Volume 13, Issue 6 , 2021 , Pages 1483-1493 ; 19324545 (ISSN) Amirshahi, A ; Hashemi, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification... 

    A novel nonlinear function evaluation approach for efficient fpga mapping of neuron and synaptic plasticity models

    , Article IEEE Transactions on Biomedical Circuits and Systems ; Volume 13, Issue 2 , 2019 , Pages 454-469 ; 19324545 (ISSN) Jokar, E ; Abolfathi, H ; Ahmadi, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    Efficient hardware realization of spiking neural networks is of great significance in a wide variety of applications, such as high-speed modeling and simulation of large-scale neural systems. Exploiting the key features of FPGAS, this paper presents a novel nonlinear function evaluation approach, based on an effective uniform piecewise linear segmentation method, to efficiently approximate the nonlinear terms of neuron and synaptic plasticity models targeting low-cost digital implementation. The proposed approach takes advantage of a high-speed and extremely simple segment address encoder unit regardless of the number of segments, and therefore is capable of accurately approximating a given...