When Nerve Cells Detect Patterns for Acquired Knowledge

Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier, Heidelberg University, arXiv, Nov 11, 2016
Commentary by Stephen Downes

Researchers are coming closer to describing exactly how knowledge is stored as patterns of connectivity in a neural network. They create from a framework "by implementing Markov chain Monte Carlo (MCMC) sampling in spiking networks of abstract model neurons." This shows how a neuron can draw an inference from a subset of the data available - a 'partial representation' - selecting from various possible interpretations. It's like: we see stripes in the jungle, is it shadows or a tiger, should we run? and we make a snap decision using this method by sampling the data. The full paper is very heavy in mathematics and neural network theory. The press release is a useful summary. Via Matthias Melcher.

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