Critical neural networks with short and long term plasticity

Abstract

In recent years self organised critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behaviour 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.

8 pages, 7 figures

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Cited by in corpus (6)

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• Three cooperative mechanisms required for recovery after brain damage
• Role of inhibitory neurons in temporal correlations of critical and supercritical spontaneous activity
• On the role of anaxonic local neurons in the crossover to continuously varying exponents for avalanche activity
• On the scaling of avalanche shape and activity power spectrum in neuronal networks
• Parameter estimation in interacting particle systems on dynamic random networks