Abstract
Neuroscience makes use of models of neurons, synapases, and learning rules that modify the efficiency of synapses in stimulating neurons. These models can be used to simulate spiking neural networks, and the standard learning rule is based on the timing of the spikes of the pre and post-synaptic neurons. This paper describes the use of these models to categorise documents by translating this Spike Timing Dependent Plasticity into an unsupervised learning rule by representing documents and categories in neurons and presenting them in specific fashion for learning and categorisation. The resulting system is comparable to other unsupervised machine learning systems. This presentation mechanism is extended to combine input feature value pairs to resolve the exclusive or problem. It is further refined to approximate co-variance of features to an arbitrary degree of precision.
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Notes
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The code can be found on http://www.cwa.mdx.ac.uk/spikeLearn/spikeLearn.html.
- 2.
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Huyck, C., Samey, C. (2021). Extended Category Learning with Spiking Nets and Spike Timing Dependent Plasticity. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_3
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