  Neocortical pyramidal neurons have many dendrites, and such dendrites are
capable of, in isolation of one-another, generating a neuronal spike. It is
also now understood that there is a large amount of dendritic growth during the
first years of a humans life, arguably a period of prodigious learning. These
observations inspire the construction of a local, stochastic algorithm based on
an earlier stochastic, Hebbian developmental theory. Here we investigate the
neuro-computational advantages and limits on this novel algorithm that combines
dendritogenesis with supervised adaptive synaptogenesis. Neurons created with
this algorithm have enhanced memory capacity, can avoid catastrophic
interference (forgetting), and have the ability to unmix mixture distributions.
In particular, individual dendrites develop within each class, in an
unsupervised manner, to become feature-clusters that correspond to the mixing
elements of class-conditional mixture distribution. Although discriminative
problems are used to understand the capabilities of the stochastic algorithm
and the neuronal connectivity it produces, the algorithm is in the generative
class, it thus seems ideal for decisions that require generalization, i.e.,
extrapolation beyond previous learning.
