Abstract
When developing learning rules, many researchers develop the rules based on biological findings outside of their network, not with the properties of their network. This means that there is a discontinuity between the network and the learning rule. This paper proposes to search within the network itself for properties that display learning rule characteristics. Within the bidirectional associative memory neural network, the transmission function works both as a node, and through mathematical analysis, displays properties similar to learning. The results of this analysis are used to create a learning rule. Using a presynaptic spike train on a postsynaptic neuron model, the derivative of the node equation successfully displays both potentiation and depression that is adjusted based on spike time. The results also show the importance of repetition in achieving long term synaptic weight changes that have an effect on the postsynaptic neuron. The simulation shows a new way to explore spike time dependent plasticity and is a step towards creating a network with the full benefits of using spiking neurons.
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Johnson, M., Chartier, S. (2017). Model Derived Spike Time Dependent Plasticity. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_40
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DOI: https://doi.org/10.1007/978-3-319-68600-4_40
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