Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity.

ABSTRACT: Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions. However, there is a lack of learning algorithms for complex SNNs with recurrent connections, comparable in efficiency with back-propagation techniques and capable of unsupervised training. Here we suppose that each neuron in a biological neural network tends to maximize its activity in competition with other neurons, and put this principle at the basis of a new SNN learning algorithm. In such a way, a spiking network with the learned feed-forward, reciprocal and intralayer inhibitory connections, is introduced to the MNIST database digit recognition. It has been demonstrated that this SNN can be trained without a teacher, after a short supervise

SUBMITTER: Demin V 

PROVIDER: S-EPMC6250118 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature