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This paper presents a spiking neural network-based investigation of the issues associated with extraction of onset, offset, and coincidental firing features from spectro-temporal data. Speech samples containing spoken isolated digits from the TI46 database are employed to demonstrate the way in which these features can be extracted using leaky integrate-and-fire spiking neurons with dynamic synapses...
The brain has the powerful capability of remembering key features of images. Based on the principle of spike timing dependent plasticity of spiking neurons and the ON/OFF pathways in the visual system, a spiking neural network is proposed to remember key features of visual images. The simulation results show that the network is capable of remembering key features according to a learning rule based...
The human brain can perform a range of complicated computations and logical reasoning using neural networks with a huge number of neurons. Since Hodgkin and Huxley proposed a set of equations to describe the electrophysiological properties of spiking neurons, various network structures of neurons have been developed through neuroscience research that can now be simulated by electronic circuits or...
This paper presents a hybrid learning algorithm for spiking neural networks (SNNs), referred to as an evolvable spiking neural network (ESNN) paradigm. The algorithm integrates a supervised and unsupervised learning approach. The unsupervised approach exploits a spike timing dependent plasticity (STDP) mechanism with explicit delay learning for multiple connections between neurons. Supervision of...
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