Description: Based on the information processing functionalities of spiking neurons, a spiking neural network model is proposed to extract features from a visual image. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields. The properties of the network are detailed in this paper. Simulation results show that the network is able to perform image feature extraction within a time interval of 100 ms. This processing time is consistent with the human visual system. The demonstrations show how the network can extract right-angle contours in a visual image. Based on this principle, many other image features can be extracted by analogy. The parallel processing mechanism of this network model is very promising for a hardware implementation based on VLSI or FPGA technology. Platform: |
Size: 288768 |
Author:sacoura31 |
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Description: This GUi implements the Eugene Izhikevich (2003) spiking equation.
Spiking Neurons simulator
Easily Simulate a Customizable Network of Spiking Leaky Integrate and Fire Neurons
Simulation of an STDP-based constructive algorithm for spiking neural networks
The Siegert Neuron has a transfer function equivalent to a leaky integrate-and-fire neuron.
Platform: |
Size: 788480 |
Author:Sina |
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Description: In order to account for the rapidity of visual processing, we explore visual coding
strategies using a one-pass feed-forward spiking neural network.We based our model
on the work of Van Rullen and Thorpe, which constructs a retinal representation
and transmission using an orthogonal wavelet transform and which is transformed
into a spike code thanks to a rank order coding scheme which provides an alternative
to the classical spike frequency coding scheme. Platform: |
Size: 209920 |
Author:sacoura
|
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Search - spiking neural network