TY  - JOUR
A1  - Papa, Bruno del
A1  - Priesemann, Viola
A1  - Triesch, Jochen
T1  - Criticality meets learning : criticality signatures in a self-organizing recurrent neural network
T2  - PLoS one
N2  - Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/44051
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-440510
SN  - 1932-6203
N1  - Copyright: © 2017 Del Papa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 12
IS  - (5): e0178683
SP  - 1
EP  - 21
PB  - PLoS
CY  - Lawrence, Kan.
ER  -