• Publications
  • Influence
Challenges for large-scale implementations of spiking neural networks on FPGAs
TLDR
The last 50 years has witnessed considerable research in the area of neural networks resulting in a range of architectures, architectures, learning algorithms and demonstrative applications. Expand
An experimental evaluation of novelty detection methods
TLDR
Novelty detection is especially important for monitoring safety-critical systems in which novel conditions rarely occur and knowledge about novelty in that system is often limited or unavailable. Expand
DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons
TLDR
A learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. Expand
Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection
TLDR
This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Expand
A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware
TLDR
This paper presents a strategy for the implementation of large scale spiking neural network topologies on FPGA devices based on the I&F conductance model. Expand
An online supervised learning method for spiking neural networks with adaptive structure
TLDR
A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically adaptive structure is presented. Expand
Edge Detection Based on Spiking Neural Network Model
TLDR
Inspired by the behaviour of biological receptive fields and the human visual system, a network model based on spiking neurons is proposed to detect edges in a visual image. Expand
SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure
TLDR
This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. Expand
Learning under weight constraints in networks of temporal encoding spiking neurons
TLDR
Weigh limitation constraints are applied to the spike time error-backpropagation (SpikeProp) algorithm for temporally encoded networks of spiking neurons. Expand
Evolutionary design of spiking neural networks.
TLDR
In this paper, a new approach for supervised training is presented with a biologically plausible architecture. Expand
...
1
2
3
4
5
...