Title: Localized random projections challenge benchmarks for bio-plausible deep learning. Abstract: Similar to models of brain-like computation, artificial deep neural networks rely
on distributed coding, parallel processing and plastic synaptic weights. Training
deep neural networks with the error-backpropagation algorithm, however, is
considered bio-implausible. An appealing alternative to training deep neural networks
is to use one or a few hidden layers with fixed random weights or trained
with an unsupervised, local learning rule and train a single readout layer with a
supervised, local learning rule. We find that a network of leaky-integrate-andfire
neurons with fixed random, localized receptive fields in the hidden layer and
spike timing dependent plasticity to train the readout layer achieves 98.1% test
accuracy on MNIST, which is close to the optimal result achievable with error-backpropagation
in non-convolutional networks of rate neurons with one hidden
layer. To support the design choices of the spiking network, we systematically
compare the classification performance of rate networks with a single hidden
layer, where the weights of this layer are either random and fixed, trained with
unsupervised Principal Component Analysis or Sparse Coding, or trained with
the backpropagation algorithm. This comparison revealed, first, that unsupervised
learning does not lead to better performance than fixed random projections for
large hidden layers on digit classification (MNIST) and object recognition (CIFAR10);
second, networks with random projections and localized receptive fields
perform significantly better than networks with all-to-all connectivity and almost
reach the performance of networks trained with the backpropagation algorithm.
The performance of these simple random projection networks is comparable to
most current models of bio-plausible deep learning and thus provides an interesting
benchmark for future approaches.