Skip to main navigation
Skip to search
Skip to main content
New Jersey Institute of Technology Home
Help & FAQ
Link opens in a new tab
Search content at New Jersey Institute of Technology
Home
Profiles
Research units
Facilities
Federal Grants
Research output
Press/Media
Spiking Neural Networks - Part II: Detecting Spatio-Temporal Patterns
Nicolas Skatchkovsky
, Hyeryung Jang
, Osvaldo Simeone
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
25
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Spiking Neural Networks - Part II: Detecting Spatio-Temporal Patterns'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
Activation Function
50%
Backpropagation
50%
Data Type
50%
Learning Rule
100%
Model Review
50%
Network Model
50%
Neuroprosthetics
50%
Nondifferentiability
50%
Recurrent Neural Network
50%
Review Paper
50%
Spatiotemporal Processing
50%
Spiking Neuron
50%
Keyphrases
Activation Function
16%
Backpropagation through Time
16%
Biological Brain
16%
Differentiable Function
16%
First Review
16%
Gradient Estimate
16%
Learning Rule
16%
Local Learning Rules
16%
Modeling Algorithm
16%
Neural Prosthesis
16%
Neuromorphic Data
16%
Neuromorphic Sensors
16%
Non-differentiability
16%
Probabilistic Model
16%
Recurrent Neural Network
16%
Review Model
16%
Rule-based
16%
Spatiotemporal Pattern
100%
Spatiotemporal Processing
16%
Spiking Neural Networks
100%
Spiking Neurons
16%
Stamping
16%
Stochastic Estimates
16%
Surrogate Gradient
16%
Training Algorithm
16%
Tweets
16%
Mathematics
Approximates
14%
Data Type
14%
Differentiability
14%
Differentiable Function
14%
Learning Rule
28%
Network Model
14%
Neural Network
100%
Spiking Neuron
14%
Stochastics
14%
Computer Science
Activation Function
20%
Backpropagation
20%
Data Type
20%
Neural Network
100%
Neural Network Model
20%
Recurrent Neural Network
20%
training algorithm
20%
Material Science
Neural Prosthesis
100%
Probabilistic Model
100%