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Masterarbeit: Does intrinsic plasticity prefer regularity? A Markov chain Monte Carlo perspective on a self-organizing recurrent neural network

Allgemein

Abstract

The self-organizing recurrent neural network (SORN) is a promising approach in modeling brain processes. It extends randomly initialized reservoir networks with binary threshold neurons by three biologically motivated plasticity mechanisms: spike-timing-dependent plasticity, synaptic normalization and intrinsic plasticity. It was already shown that SORN outperforms static reservoir networks and is able to reproduce numerous experimental findings. In particular, recent findings show that spontaneous activity has a special role in this deterministic network. As former approaches used specific input patterns, in this thesis, input patterns were generalized and sampled from systematically chosen Markov chains. Results show that the performance of the network depends on the structure of the Markov chain. If chains contain some less probable and irregular states, their performance is lower and closer to results from static reservoir networks. Finally, it was hypothesized that intrinsic plasticity is the cause for this behavior, due to arising spontaneous activity in less active neurons. It was concluded that SORN does not outperform static reservoir networks in every case and that the performance is not necessarily independent from the chosen input pattern. It remains to be shown if this effect does also occur in natural networks.

 
hu-berlin/statistik/module/masterarbeit.txt · Zuletzt geändert: 2020/09/04 11:05 von carlo
 
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