Unconventional Criticality in The Stochastic Wilson-Cowan Model
criticality; neuronal avalanches; Wilson-Cowan model; universality classes; directed
percolation; scaling relation breakdown.
The Wilson-Cowan model serves as a classic framework for comprehending the collective
neuronal dynamics within networks comprising both excitatory and inhibitory units. Extensively
employed in literature, it facilitates the analysis of collective phases in neural networks at a
mean-field level, i.e., when considering large fully connected networks. To study fluctuationinduced
phenomena, the dynamical model alone is insufficient; to address this issue, we need
to work with a stochastic rate model that is reduced to the Wilson-Cowan equations in a
mean-field approach. Throughout this thesis, we analyze the resulting phase diagram of the
stochastic Wilson-Cowan model near the active to quiescent phase transitions. We unveil eight
possible types of transitions that depend on the relative strengths of excitatory and inhibitory
couplings. Among these transitions are second-order and first-order types, as expected, as
well as three transitions with a surprising mixture of behaviors. The three bona fide secondorder
phase transitions belong to the well-known directed percolation universality class, the
tricritical directed percolation universality class, and a novel universality class called “Hopf
tricritical directed percolation", which presents an unconventional behavior with the breakdown
of some scaling relations. The discontinuous transitions behave as expected and the hybrid
transitions have different anomalies in scaling across them. Our results broaden our knowledge
and characterize the types of critical behavior in excitatory and inhibitory networks and help us
understand avalanche dynamics in neuronal recordings. From a more general perspective, these
results contribute to extending the theory of non-equilibrium phase transitions into quiescent
or absorbing states.