Date of Award
Bachelor of Science (B.S.) in Electrical Engineering and University Honors
Electrical and Computer Engineering
Dendrites, Transfer functions, Neural networks, Machine learning
Dendritic branch operations in pyramidal neurons are well understood in-vivo but their potential as computational assets in deep neural networks has not been explored. The pre-processing which dendrites perform may be able to decrease the error of an artificial neuron because each dendrite serves as an independent filtering mechanism which may prevent false positives. In order to test this hypothesis, a fully-connected layer implementing the dendritic transfer function is defined and used to replace the final fully-connected layer used in a standard CNN (convolutional neural network). Results show that the defined algorithm is not able to predict better than chance and possible causes are discussed. A framework for developing future dendritic layers is established.
Musil, Mark Robert, "A Dendritic Transfer Function in a Novel Fully-connected Layer" (2019). University Honors Theses. Paper 702.