Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

ABSTRACT: The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to

SUBMITTER: Burbank KS 

PROVIDER: S-EPMC4669146 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature