DOI: 10.5061/dryad.kv5r4
title: Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons

description: 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.
description: Archive of code and
 parameter filesA zip file containing
 the code used to run the simulations, as well as the specific parameter
 files used to generate the data shown in the paper.Archive.zipMNIST main params network over timeThe data file where network state is recorded throughout the simulation training period, for the MNIST parameters used for the main results in the paper.MNIST_pp_0p001.hdf5MNIST main params calculated valuesA data file containing calculated values measuring quantities such as reconstruction performance, average neuronal activity, etc for different points in the training sequence. These values were used directly to generate the figures in the manuscript.MNIST_pp_0p03.pkl.pklNatural images main params network over timeThe data file where network state is recorded throughout the simulation training period, for the natural image parameters used for the main results in the paper.Natim.hdf5Natural images very sparse params network over timeThe data file where network state is recorded throughout the simulation training period, for the very sparse natural image parameters used for the Figure 10b in the paper.pp_0p001.hdf5Natural image very sparse main params calculated valuesA data file containing calculated values measuring quantities such as reconstruction performance, average neuronal activity, etc for different points in the training sequence. These values were used directly to generate Figure 10b in the manuscript.pp_0p001.pklMNIST very sparse params network over timeThe data file where network state is recorded throughout the simulation training period, for the very sparse MNIST parameters used for Figure 10a in the paper.MNIST_pp_0p0010.hdf5MNIST very sparse params calculated valuesA data file containing calculated values measuring quantities such as reconstruction performance, average neuronal activity, etc for different points in the training sequence. These values were used directly to generate Figure 10a in the manuscript.MNIST_pp_0p001.pklMNIST non-sparse params network over timeThe data file where network state is recorded throughout the simulation training period, for the MNIST parameters used for the Figure 10c in the paper.MNIST_pp_0p30.hdf5MNIST non-sparse params calculated valuesA data file containing calculated values measuring quantities such as reconstruction performance, average neuronal activity, etc for different points in the training sequence. These values were used directly to generate Figure 10c in the manuscript.MNIST_pp_0p3.pklNatural images main params calculated valuesA data file containing calculated values measuring quantities such as reconstruction performance, average neuronal activity, etc for different points in the training sequence. These values were used directly to generate the figures in the manuscript.pp_0p02_main_results.pkl