First Advisor

Dan Hammerstrom

Date of Award

Winter 2020

Document Type


Degree Name

Bachelor of Science (B.S.) in Computer Science and University Honors


Computer Science




Natural computation, Neural networks (Computer science)




We present research on an implementation of a biologically inspired Bayesian Confidence Propagation Neural Network (BCPNN). Based on previous work by Christopher Johansson and Anders Lansner, our implementation seeks to test and understand the various properties of this model. The floating-point implementation we built uses discrete time and bit-vectors as input/output. We found that the column based BCPNN model is able to memorize a decent number of input vectors and is able to restore noisy versions of these vectors with relatively high accuracy. We examine the model’s capacity, noise recovery ability and cross-column connection influence, among other attributes. The clearest trends that we found were in the capacity and noise recovery properties of the model, while the influence of cross-column connections was less clear. Further research and development of this model implementation is needed to increase speed, capacity and error correction capabilities.


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