Fork the consciousness, or download the project and create your own.

Anders Lansner

KTH Royal Institute of Technology

Builder The claim is about what the hardware does, and the philosophy is left open

Anders Lansner is a Swedish computational neuroscientist at KTH Royal Institute of Technology who created the Bayesian Confidence Propagation Neural Network, BCPNN, a correlation-based learning rule grounded in Bayesian updating. BCPNN has been implemented to correspond to neural and synaptic processes in the human cortex and has been used extensively in detailed spiking models of cortical associative memory, as well as in machine learning applications.

The rule’s move into hardware came in the 2021 Frontiers in Neuroscience paper first-authored by Deyu Wang, which mapped BCPNN onto a memristor model to escape the von Neumann bottleneck that makes digital implementations of synaptic storage expensive. Lansner’s coauthorship places his model at the center of the spike-native hardware line this site tracks.

This site’s Builder category refers to his authorship of the learning rule and its model lineage. The work concerns memory and cortical dynamics, and it makes no claim about consciousness. This site covers the mapping in the memristive neurons analysis.

Known for. The BCPNN learning rule, spiking models of cortical associative memory, BCPNN-inspired silicon

Coverage on this site

Related researchers