  It is still not fully understood exactly how neural networks are able to
solve the complex tasks that have recently pushed AI research forward. We
present a novel method for determining how information is structured inside a
neural network. Using ablation (a neuroscience technique for cutting away parts
of a brain to determine their function), we approach several neural network
architectures from a biological perspective. Through an analysis of this
method's results, we examine important similarities between biological and
artificial neural networks to search for the implicit knowledge locked away in
the network's weights.
