Homeostatic Plasticity in a Leaky Integrate and Fire Neuron Using Tunable Leak
Author:
Affiliation:
1. The University of Tennessee, Knoxville,Min H. Kao Department of Electrical Engineering & Computer Science,Knoxville,TN,USA,37996
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10405424/10405847/10406066.pdf?arnumber=10406066
Reference18 articles.
1. Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware
2. Device-aware Circuit Design for Robust Memristive Neuromorphic Systems with STDP-based Learning
3. Neuromorphic Character Recognition System With Two PCMO Memristors as a Synapse
4. Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level
5. Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks
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1. Hardware software co-design for leveraging STDP in a memristive neuroprocessor;Neuromorphic Computing and Engineering;2024-05-22
2. HfO2-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19
3. Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks;Scientific Reports;2024-04-17
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