Intrinsic plasticity in autonomous recurrent neural networks

In recent years there have been many studies of the possible occurrence of selforganized criticality in neural networks with synaptic plasticity. Most of them have concluded that various form of synaptic adaptation drive the network dynamics far below critical point (Siri 2007), in other words they over-regulate the neural activity. Hence, there should exist an additional mechanism, maintaining the desired level of excitability in neural networks and preventing the dynamical states which are non-reactive to external influences.

Such counter mechanism can be found in a form of adaptation observed in biological neurons, working on the level of the membrane elements. This non-synaptic adjustment, manifested as a change in neuron excitability (Mozzachiodi 2010), is also known as intrinsic plasticity.

We have studied a previously proposed model of intrinsic plasticity (Triesch 2005), and its influence on the dynamical properties of autonomous recurrent neural networks with discrete time rate encoding neurons. The introduction of intrinsic plasticity results in ongoing and self-sustained neural activities with non trivial dynamical states. For large networks, one observes three self-organized distinct phase states. Depending on the specied network parameters, the neural activity exhibits either chaotic, intermittent bursting, or synchronized oscillatory behavior. These results show that non-synaptic adaptation plays an important role in the formation of complex patterns of neural activity.