What is the best model to use for playing around with the evolution of neural network topologies? I expect building a neural network with matrices and dot products isn't going to be so useful. Should I essentially build a directed graph, and recursively follow it to perform forward and backpropegation? That seems very slow and inefficient, and yet that's the best way I can see to keep the topology very mutable.
Can we design a system that automates the search for the optimal design? Maybe it can find surprising solutions. What would be the minimal neural net that learns a given task and also learns to improve itself (the minimal Gödel machine)? It would be as if we endowed neural nets with the power of procreation.
Neural crest populations along the embryonic body axis of vertebrates differ in developmental potential and fate, so that only the cranial neural crest can contribute to the craniofacial skeleton in vivo. Using axial-level specific enhancers to isolate and perform genome-wide profiling of the cranial versus trunk neural crest in chick embryos, we identified and characterized regulatory relationships between a set of cranial-specific transcription factors. Introducing components of this circuit into neural crest cells of the trunk alters their identity and endows these cells with the ability to give rise to chondroblasts in vivo. Our results demonstrate that gene regulatory circuits that support the formation of particular neural crest derivatives may be used to reprogram specific neural crest–derived cell types.