  Artificial and natural neural network models are a new toolkit which could be
potentially have been used for clarifying of complex brain functions. To attend
this goal, such models need to be neurobiologically realistic. However,
although neural networks have advanced keenly in recent decades their strict
similarity in aspects of brain anatomy and physiology is imperfect. In this
work we discuss different types of neural models, including localist, attractor
and deep network models, and also identify aspects under which their biological
credibility can be improved. These conditions range from the choice of neuron
models and of mechanisms of synaptic plasticity and learning to implementation
of inhibition and control, along with network architectures (modularity,
connectivity). We highlight recent advances in biologically inspired neural
network models and their constraints.
