NEUROPOIESIS - PROPOSAL FOR A CONNECTIONISTIC NEUROBIOLOGY

Given current assumptions about the biology of neural organization, so me connectionists believe that it may not be possible to accurately mo del the brain's neural architecture. We have identified five restricti ve neurobiological dogmas that we believe have limited the exploration of more fundamental correlations between computational and biological neural networks. We postulate that: 1) the dendritic tree serves as a synapse storage device rather than a simple summation device; 2) conn ection strength between neurons depends on the number and location of synapses of similar weight, not on synapses of variable weights; 3) ax onal sprouting occurs regularly in adult organisms; 4) the postsynapti c genome directly controls the presynaptic cell via mRNA, rather than indirectly by the expression of NCAMs, reverse neurotransmitters, etc. ; 5) dendritic spines serve a trophic function by controlling developm ent of new sprouts via a process we term retroduction. We entertain an alternative formulation of a computational neural element that is ful ly consistent with modern neuroscience research. We then show how our model neuron can learn under Hebbian conditions, and extend the model to explain non-Hebbian, one-trial learning. This work is significant b ecause by stretching the theoretical boundaries of modern neuroscience , we show how connectionists can potentially create new, more biologic ally-based neural elements which, when, interconnected into networks, exhibit not only properties of existing backpropagation networks, but other physiological properties as well.