Development/Plasticity/Repair Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed to solve for example the binding problem.However, it is not clear how single neurons could acquire such functionality in a self-organizedmanner, becausemost theoretical studies of synaptic plasticity and learning concen-trate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticitymechanisms in single neurons has emerged fromexperiments. In particular, newexperimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent spike-timing-dependent plasticity and branch-strength potentiation, could be integrated to self-organize nonlinear neural computationswith dendritic spikes.We provide a mathematical proof that, in a simplified setup, these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to becomemember of several neuronal ensembles and to acquire nonlinear computational capabilities, such as the capability to bindmultiple input features. Hence, our results suggest that nonlinear neural computationmay self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms