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Description | Identify Repeating Patterns using Spiking Neural Networks in Kaizou ? Identify Repeating Patterns using Spiking Neural Networks in Tensorflow 26 Jul 2018 by David Corvoysier Spiking neural networks ( SNN ) are the 3 |
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US$919,953
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Kaizou ? Identify Repeating Patterns using Spiking Neural Networks in Tensorflow 26 Jul 2018 by David Corvoysier Spiking neural networks ( SNN ) are the 3rd generation of neural networks. SNN do not react on each stimulus, but rather accumulate inputs until they reach a threshold potential and generate a ‘spike’. Because of their very nature, SNNs cannot be trained like 2nd generation neural networks using gradient descent. Spike Timing Dependent Plasticity ( STDP ) is a biological process that inspired an unsupervised training method for SNNs. In this article, I will provide an illustration of how STDP can be used to teach a single neuron to identify a repeating pattern in a continuous stream of input spikes. For this, I will reproduce the STDP experiments described in Masquelier & Thorpe (2008) using Tensorflow instead of Matlab. LIF neuron model The LIF neuron model used in this experiment is based on Gerstner’s Spike Response Model . At every time-step, the neuron membrane |
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