Knol, L. (2020) Dynamic coding in a large-scale, functional, spiking-neuron model. Bachelor's Thesis, Artificial Intelligence.
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Abstract
A functional, large-scale, spiking-neuron model of working memory (WM) was adapted to display the patterns often cited in EEG studies as evidence of dynamic coding. The model had a mechanism for temporarily adjusting its own inter-neuron connection strengths following network activation, which served as the main memory mechanism. As for dynamic coding: It is a phenomenon observed in the human brain, in which information is represented in a particular way at time step $t$, but is represented differently at time step $t + 1$, while, crucially, the information itself does not change. In a previous, human EEG study, data were obtained that showed such dynamic coding patterns. Two experiments from that study were conducted with the model, and the model results were compared to the corresponding human data. The comparisons showed that the model and human coding patterns display many similarities, but also some differences. Moreover, the model performed not as well as humans did. Eventually, however, it was concluded that the model did in fact display dynamic coding, which would mean that dynamic coding might simply be a property of any self-modifying network. This calls for a perspective on dynamic coding that is slightly more modest than what is suggested in the existing literature.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Borst, J.P. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 27 Jul 2020 08:44 |
| Last Modified: | 27 Jul 2020 08:44 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/22884 |
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