scholarly article | Q13442814 |
P818 | arXiv ID | 1806.01423 |
P8978 | DBLP publication ID | journals/fini/HazanSKPSSK18 |
P356 | DOI | 10.3389/FNINF.2018.00089 |
P932 | PMC publication ID | 6315182 |
P698 | PubMed publication ID | 30631269 |
P50 | author | Hananel Hazan | Q90990275 |
P2093 | author name string | Robert Kozma | |
Hava T Siegelmann | |||
Daniel J Saunders | |||
Darpan T Sanghavi | |||
Devdhar Patel | |||
Hassaan Khan | |||
P2860 | cites work | Supervised Learning Based on Temporal Coding in Spiking Neural Networks | Q50430814 |
Unsupervised Feature Learning With Winner-Takes-All Based STDP. | Q52576143 | ||
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks. | Q55243903 | ||
Prefrontal cortex as a meta-reinforcement learning system | Q62496126 | ||
First-Spike-Based Visual Categorization Using Reward-Modulated STDP | Q89556746 | ||
The Brian simulator | Q27500449 | ||
Deep learning | Q28018765 | ||
Toward an Integration of Deep Learning and Neuroscience | Q28595812 | ||
Python as a federation tool for GENESIS 3.0 | Q28732679 | ||
Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs | Q29618758 | ||
ANNarchy: a code generation approach to neural simulations on parallel hardware | Q30659592 | ||
NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data | Q30752303 | ||
Nengo: a Python tool for building large-scale functional brain models | Q31148248 | ||
PyNN: A Common Interface for Neuronal Network Simulators | Q33405758 | ||
NEURON and Python | Q33406510 | ||
Equation-oriented specification of neural models for simulations | Q35097160 | ||
GeNN: a code generation framework for accelerated brain simulations. | Q35887778 | ||
Training Deep Spiking Neural Networks Using Backpropagation | Q39161754 | ||
Software for Brain Network Simulations: A Comparative Study | Q41063782 | ||
Unsupervised learning of digit recognition using spike-timing-dependent plasticity | Q41964489 | ||
Spiking neural network connectivity and its potential for temporal sensory processing and variable binding | Q42148653 | ||
STDP-based spiking deep convolutional neural networks for object recognition. | Q47564277 | ||
Simple model of spiking neurons | Q47832705 | ||
Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. | Q47841509 | ||
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification | Q49632686 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P4510 | describes a project that uses | Python | Q28865 |
scikit-learn | Q1026367 | ||
Neuron | Q7002467 | ||
P407 | language of work or name | English | Q1860 |
P921 | main subject | machine learning | Q2539 |
Python | Q28865 | ||
biomedical engineering | Q327092 | ||
P6104 | maintained by WikiProject | WikiProject Software | Q15659621 |
P304 | page(s) | 89 | |
P577 | publication date | 2018-12-12 | |
P1433 | published in | Frontiers in Neuroinformatics | Q25474550 |
P1476 | title | BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python | |
P478 | volume | 12 |
Q92711585 | A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents |
Q94464021 | Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing |
Q97516545 | Exploring the Connection Between Binary and Spiking Neural Networks |
Q92237494 | SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron |
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