@article{TEXTUAL,
      recid = {7480},
      author = {Burbank, Kendra S.},
      title = {Mirrored STDP Implements Autoencoder Learning in a Network  of Spiking Neurons},
      journal = {PLOS Computational Biology},
      address = {2015-12-03},
      number = {TEXTUAL},
      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 identical changes at  feedforward and feedback connections. Here, we study a  biologically realistic network of integrate-and-fire  neurons with anatomical connectivity and synaptic  plasticity that closely matches that observed in cortical  sensory areas. Our choice of synaptic plasticity rules is  inspired by recent experimental and theoretical results  suggesting that learning at feedback connections may have a  different form from learning at feedforward connections,  and our results depend critically on this novel choice of  plasticity rules. Specifically, we propose that plasticity  rules at feedforward versus feedback connections are  temporally opposed versions of spike-timing dependent  plasticity (STDP), leading to a symmetric combined rule we  call Mirrored STDP (mSTDP). We show that with mSTDP, our  network follows a learning rule that approximately  minimizes an autoencoder loss function. When trained with  whitened natural image patches, the learned synaptic  weights resemble the receptive fields seen in V1. Our  results use realistic synaptic plasticity rules to show  that the powerful autoencoder learning algorithm could be  within the reach of real biological networks.},
      url = {http://knowledge.uchicago.edu/record/7480},
}