Essays about: "Leaky Integrate and Fire"

Showing result 1 - 5 of 7 essays containing the words Leaky Integrate and Fire.

  1. 1. Comparing energy efficiency of Leaky integrate-and-fire and Spike response neuron models in Spiking Neural Networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Majd Dawli; Imran Bahed Diva; [2023]
    Keywords : ;

    Abstract : Spiking Neural Networks (SNNs) are a type of neural network that is designed to mimic the way neurons function in our brains. While there have been notable advancements in developing SNNs, energy consumption hasn't been studied to the same extent. This gets especially relevant with steadily increasing network sizes. READ MORE

  3. 2. Fractional proportional-integrative-derivative controller : Design, analysis and applications to DC motor and single neuron spiking

    University essay from Uppsala universitet/Institutionen för elektroteknik

    Author : Hassan Soltani; [2023]
    Keywords : Fractional; FPID;

    Abstract : This thesis aims to explore and compare the performance of fractional proportional-integrative-derivative controllers in the DC motor system and the fractional leaky integrate and fire (FLIF) model for neuron spike signals. The objective is to determine whether FPID controllers exhibit superior performance and significant improvements in control compared to PID controllers. READ MORE

  4. 3. Exploring Column Update Elimination Optimization for Spike-Timing-Dependent Plasticity Learning Rule

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ojasvi Singh; [2022]
    Keywords : Spike-Timing Dependent Plasticity; neuromorphic computing; Hebbian Learning; Spiking Neural Networks; memory optimization.; Spike-Timing Beroende Plasticitet; neuromorfisk beräkning; Hebbiansk inlärning; Spiking Neural Networks; Minnes optimering;

    Abstract : Hebbian learning based neural network learning rules when implemented on hardware, store their synaptic weights in the form of a two-dimensional matrix. The storage of synaptic weights demands large memory bandwidth and storage. READ MORE

  5. 4. Exploring the column elimination optimization in LIF-STDP networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mingda Sun; [2022]
    Keywords : SpikingNeuralNetwork SNN ; neuromorphiccomputing; memoryoptimization; Hebbian learning; Spike-timing-depend plasticity STDP learning; Spiking Neural Network SNN ; neuromorphic computing; minnesoptimering; Hebbisk inlärning; spike-timing-depend plasticity STDP inlärning;

    Abstract : Spiking neural networks using Leaky-Integrate-and-Fire (LIF) neurons and Spike-timing-depend Plasticity (STDP) learning, are commonly used as more biological possible networks. Compare to DNNs and RNNs, the LIF-STDP networks are models which are closer to the biological cortex. READ MORE

  6. 5. Universality and Individuality in Recurrent Networks extended to Biologically inspired networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Nishant Joshi; [2020]
    Keywords : Recurrent Neural Networks; rate Networks; Fixed Points; Dynamical Systems; Leaky Integrate and Fire; Computational Neuroscience;

    Abstract : Activities in the motor cortex are found to be dynamical in nature. Modeling these activities and comparing them with neural recordings helps in understanding the underlying mechanism for the generation of these activities. For this purpose, Recurrent Neural networks or RNNs, have emerged as an appropriate tool. READ MORE