Abstract—Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function ap-proximators. Spiking neural networks offer several advantages over sigmoidal networks, because they can approximate the dynamics of biological neuronal networks, and can potentially reproduce the computational speed observed in biological brains by enabling temporal coding. On the other hand, the effec-tiveness of spiking neural network training algorithms is still far removed from that exhibited by backpropagating sigmoidal neural networks. This paper presents a novel algorithm based on reward-modulated spike-timing-dependent plasticity that is biologically plausible and capable of training a spiking neural network to learn the exclusive-or (XOR) computation, through rate-based coding. The results show that a spiking neural network model with twenty-three nodes is able to learn the XOR gate accurately, and performs the computation on time scales of milliseconds. Moreover, the algorithm can potentially be verified in light-sensitive neuronal networks grown in vitro by determining the spikes patterns that lead to the desired synaptic weights computed in silico when induced by blue light in vitro. I.

. CC-BY-NC-ND 4.0 International licensepeer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not. http://dx.doi.org/10.1101/015974doi: bioRxiv preprint first posted online Mar. 4, 2015; 2 Smooth endoplasmic reticulum (SER) forms a membranous network that extends throughout neurons. SER regulates intracellular calcium and the posttranslational modification and trafficking of membrane and proteins. As the structure of dendritic SER shifts from a tubular to a more complex, branched form, the movement of membrane cargo slows and delivery to nearby spines increases. Here we discovered changes in the structural complexity of SER that have important functional implications during long-term potentiation (LTP) in adult rat hippocampus. By 2 hours after the induction of LTP with theta-burst stimulation, synapse enlargement was greatest on spines that contained SER. More spines had an elaborate spine apparatus than a simple tubule of SER. The SER in dendritic shafts became more complex beneath spines with both polyribosomes and SER, and less complex along aspiny dendritic regions. The findings suggest that local changes in dendritic SER support enhanced growth of specific synapses during LTP.