Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons

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.