@article{vk18,
author = {Michiels van KessenichL, and LukovićM, and de ArcangelisL, and HerrmannHJ,},
pages = {7},
title = {Critical neural networks with short- and long-term plasticity},
volume = {97},
note = {Articolo in rivista},
issn = {2470-0045},
journal = {PHYSICAL REVIEW. E},
year = {2018},
wosId = {WOS:000428166300007},
abstract = {In recent years self organized critical neuronal models have provided insights regarding the origin of the
experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses,
as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as
Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural
systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms,
acting on two different time scales. The measured avalanche statistics are compatible with experimental results
for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons.
The time series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The
presence of long-term plasticity gives the system the ability to learn binary rules such as XOR, providing the
foundation of future research on more complicated tasks such as pattern recognition.}
}