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There is a huge body of literature on axon growth cone guidance which will give you some insights into how the biology works. Unfortunately, incorporating it all into a model is probably going to make it unwieldy unless your express purpose is to model the physiology, which doesn't seem like the case. Here are some references: Hong K, Nishiyama M. ...


8

I don't know of any NN algorithms that match your definition entirely, and I have looked for them (previously and recently). Here are some papers that I think are close or in the direction that you are exploring. Using theoretical models to analyze neural development (review) An Instruction Language for Self-Construction in the Context of Neural Networks ...


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For a comprehensive review, see: Berg, D.A., Belnoue, L.,Song,H., Simon A. (2013) Neurotransmitter-mediated control of neurogenesis in the adult vertebrate brain. Development, 140(12), 2548-2561. [DOI]. Briefly, active adult neurogenesis is confined to two distinct locations: the subventricular zone (SVZ) of the lateral ventricles in the ...


6

For the dentate gyrus, which is probably more closely analogous to a feedforward hidden layer in a memory network, here are some answers: Axon and dendrite connectivity is essentially local and can probably be assumed to be initially random within that local region. That is, a neuron integrating into the DG at the midpoint (along the long hippocampal ...


1

I don't know of a particular network model for this job (so my answer will be an incomplete one), but I believe that any Hebbian learning based associative memory can easily be structured to simulate neurogenesis. These unsupervised networks are actually nonlinear dynamic systems that can be understood in terms of their phase spaces. Phase space is the ...



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