  Artificial Neural Networks (ANNs) inspired by biology are beginning to be
widely used to model behavioral and neural data, an approach we call
neuroconnectionism. ANNs have been lauded as the current best models of
information processing in the brain, but also criticized for failing to account
for basic cognitive functions. We propose that arguing about the successes and
failures of a restricted set of current ANNs is the wrong approach to assess
the promise of neuroconnectionism. Instead, we take inspiration from the
philosophy of science, and in particular from Lakatos, who showed that the core
of scientific research programmes is often not directly falsifiable, but should
be assessed by its capacity to generate novel insights. Following this view, we
present neuroconnectionism as a cohesive large-scale research programme
centered around ANNs as a computational language for expressing falsifiable
theories about brain computation. We describe the core of the programme, the
underlying computational framework and its tools for testing specific
neuroscientific hypotheses. Taking a longitudinal view, we review past and
present neuroconnectionist projects and their responses to challenges, and
argue that the research programme is highly progressive, generating new and
otherwise unreachable insights into the workings of the brain.
