Brain-inspired hierarchical spiking neural network using unsupervised STDP rule for image classification

2018 ◽  
Author(s):  
Jiaxing Liu ◽  
Hong Huo ◽  
Weitai Hu ◽  
Tao Fang
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Spiking Neural Network ◽  
Stdp Rule

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

scholarly journals Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 165 ◽  
Author(s):  
Krishnamurthy V. Vemuru
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Image Classification ◽  
Spiking Neural Network ◽  
Edge Detector ◽  
Time Step ◽  
Biologically Inspired ◽  
Total Exposure Time ◽  
Canny Edge ◽  
Low Exposure

We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

scholarly journals Simplified spiking neural network architecture and STDP learning algorithm applied to image classification

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Taras Iakymchuk ◽  
Alfredo Rosado-Muñoz ◽  
Juan F Guerrero-Martínez ◽  
Manuel Bataller-Mompeán ◽  
Jose V Francés-Víllora
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Spiking Neural Network ◽  
Neural Network Architecture

scholarly journals A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons

2021 ◽  
Vol 12 (03) ◽  
pp. 25-33
Author(s):  
Mario Antoine Aoun
Keyword(s):  
Neural Network ◽  
Chaos Theory ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Stdp Rule

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.

scholarly journals Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network

2021 ◽  
Author(s):  
Jiankun Chen ◽  
Xiaolan Qiu ◽  
Chuanzhao Han ◽  
Yirong Wu
Keyword(s):  
Neural Network ◽  
Unsupervised Learning ◽  
Image Classification ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Sar Image ◽  
The Brain

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

scholarly journals Efficient VLSI Architecture for Odor Recognition with a Spiking Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 6-9
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Vlsi Architecture ◽  
Spike Timing ◽  
Time Difference ◽  
Spiking Neural Network ◽  
Cortical Cells ◽  
Odor Recognition ◽  
Stdp Rule

In this paper spiking neural network (SNN) is presented which can discriminate odor data. Spike timing dependent synaptic plasticity (STDP) means a plasticity which is controlled by the presynaptic and postsynaptic spikes time difference. Using this STDP rule the synaptic weights are modified after the mitral and before the cortical cells. In order to determine whether the circuit has correctly identified the odor the SNN has either a high or a low response at the output for any odor given as the input.

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