A spiking neuron model: applications and learning
نویسندگان
چکیده
منابع مشابه
A spiking neuron model: applications and learning
This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal dynamics at the neuron level by modelling...
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In this paper we review some of our recent results on discrete-state spiking neuron models. The discrete-state spiking neuron model is a wired system of shift registers and can generate various spike-trains by adjusting the pattern of the wirings. In this paper we show basic relations between the wiring pattern and characteristics of the spike-train. We also show a learning algorithm which util...
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Introduction: Biological neurons communicate via sequences of calibrated pulses or spikes. The behaviour of spiking neurons is the following: input spikes from pre-synaptic neurons are weighted and summed up yielding a value called membrane potential. The membrane potential is time dependent and decays when no spikes are received by the neuron. If however spikes excite the membrane potential su...
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ژورنال
عنوان ژورنال: Neural Networks
سال: 2002
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(02)00034-5