New approximation method for smooth error backpropagation in a quantron network
Copyright policy )New approximation method for smooth error backpropagation in a quantron network
In this work, we propose a new approximation method to perform error backpropagation in a quantron network while avoiding the silent neuron problem that usually affects networks of realistic neurons. In our experiments, we train quantron networks to solve the XOR problem and other nonlinear classification problems. We achieve this while using less parameters than the number necessary to solve the same problems with networks of perceptrons or spiking neurons.
- Polytechnique Montréal Canada
- University of Montreal Canada
Neurons, smooth approximation, spiking neurons, Cognitive Neuroscience, Models, Neurological, Learning and adaptive systems in artificial intelligence, quantrons, Advanced Memory and Neural Computing, Neural networks for/in biological studies, artificial life and related topics, Neural Networks and Reservoir Computing, classification, Artificial Intelligence, Neural dynamics and brain function, Neural Networks, Computer, Electrical and Electronic Engineering, Algorithms, backpropagation
Neurons, smooth approximation, spiking neurons, Cognitive Neuroscience, Models, Neurological, Learning and adaptive systems in artificial intelligence, quantrons, Advanced Memory and Neural Computing, Neural networks for/in biological studies, artificial life and related topics, Neural Networks and Reservoir Computing, classification, Artificial Intelligence, Neural dynamics and brain function, Neural Networks, Computer, Electrical and Electronic Engineering, Algorithms, backpropagation
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