%0 Journal Article %1 WOS:000428166300007 %A van Kessenich, Michiels L %A Lukovic, M %A de Arcangelis, L %A Herrmann, H J %C ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA %D 2018 %I AMER PHYSICAL SOC %J PHYSICAL REVIEW E %K imported %N 3 %R 10.1103/PhysRevE.97.032312 %T Critical neural networks with short- and long-term plasticity %V 97 %X 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.

@article{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.}, added-at = {2022-05-23T20:00:14.000+0200}, address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA}, author = {van Kessenich, Michiels L and Lukovic, M and de Arcangelis, L and Herrmann, H J}, biburl = {https://www.bibsonomy.org/bibtex/2f883ea2dd2c567e8b544ac6f804d9920/ppgfis_ufc_br}, doi = {10.1103/PhysRevE.97.032312}, interhash = {0b8caf952fb23bfe1290f98084339e24}, intrahash = {f883ea2dd2c567e8b544ac6f804d9920}, issn = {2470-0045}, journal = {PHYSICAL REVIEW E}, keywords = {imported}, number = 3, publisher = {AMER PHYSICAL SOC}, pubstate = {published}, timestamp = {2022-05-23T20:00:14.000+0200}, title = {Critical neural networks with short- and long-term plasticity}, tppubtype = {article}, volume = 97, year = 2018 }