Fabrizio Pittorino — Università di Parma #
Onset of chaos and criticality in neural networks with synaptic plasticity #
Models of neural networks are a useful tool to investigate general mechanisms
underlying dynamical regimes observed in neuronal systems, such as
collective activity oscillations and the presence of critical avalanches in the cortex.
We consider a reliable model for cortical neurons, a network of purely excitatory leaky
integrate-and-fire neurons connected via the Tsodyks-Uziel-Markram (TUM) dynamical model for synaptic plasticity.
We observe a transition to chaos as a function of the coupling strength and of the synaptic time
scale, and we investigate this regime through a heterogeneous mean field approach.
In the limit of a fully connected network, we are able to reduce the dynamics to a one-dimensional map
that clarifies the mechanism underlying the transition. We observe that
the onset of chaos in the fully connected model corresponds to a bursty phase on a disordered topology.
We characterize the latter phase as strongly correlated and potentially carrying a
larger amount of information than quasi-synchronous and asynchronous regimes.