  Neural networks that can capture key principles underlying brain computation
offer exciting new opportunities for developing artificial intelligence and
brain-like computing algorithms. Such networks remain biologically plausible
while leveraging localized forms of synaptic learning rules and modular network
architecture found in the neocortex. Compared to backprop-driven deep learning
approches, they provide more suitable models for deploying on neuromorphic
hardware and have greater potential for scalability on large-scale computing
clusters. The development of such brain-like neural networks depends on having
a learning procedure that can build effective internal representations from
data. In this work, we introduce and evaluate a brain-like neural network model
capable of unsupervised representation learning. It builds on the Bayesian
Confidence Propagation Neural Network (BCPNN), which has earlier been
implemented as abstract as well as biophyscially detailed recurrent attractor
neural networks explaining various cortical associative memory phenomena. Here
we developed a feedforward BCPNN model to perform representation learning by
incorporating a range of brain-like attributes derived from neocortical
circuits such as cortical columns, divisive normalization, Hebbian synaptic
plasticity, structural plasticity, sparse activity, and sparse patchy
connectivity. The model was tested on a diverse set of popular machine learning
benchmarks: grayscale images (MNIST, Fashion-MNIST), RGB natural images (SVHN,
CIFAR-10), QSAR (MUV, HIV), and malware detection (EMBER). The performance of
the model when using a linear classifier to predict the class labels fared
competitively with conventional multi-layer perceptrons and other
state-of-the-art brain-like neural networks.
