Symbol Based Self Guided Neural Architecture Search in a Spiking Neural Network

author Tofara Moyo

# abstract

A spiking neural networks neurons can viewed as feature detectors or alternatively instances of hieroglyphic symbols defined by the associated features they represent .The set of activations at any time step then represent a document written in this language. If we feed this information from the previous time step back to the spiking neural network at each time step ,the network will navigate its own space of internal representations and form a grounded language in which to analyze its own internal states and to guide their evolution. We describe this method and how it could be used by the algorithm to plan and design connections and critic its own thought processes if all of this increases the expected reward

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An Algorithm to Optimize the Routing of Neurons in an Artificial Spiking Neural Network

We describe an algorithm that determines the optimal connectivity of a spiking neural network such that it can reach the lowest possible loss for that number of neurons. If the spiking network was as big as the human brain, then a local minimum would have it connected just like a typical brain is connected after the algorithm is done. Giving it all of its functionality to the extent that artificial spiking neurons mimic biological ones.