Increased Firing Irregularity as an Emergent Property of Neural-State Transition in Monkey Prefrontal Cortex
Figure 1
Network states and firing irregularity.
(A) Schematic diagram for attractor landscapes and state transitions of dynamical systems. Each row demonstrates representative state transitions or bifurcations. From top to bottom: pitch fork, saddle-node, and Hopf bifurcations. Regardless of the type of bifurcation, dynamical systems exhibit common behavior. Far from the critical point (left), systems are resilient to perturbations, but when systems are closer to the critical point (middle), they lose resilience, become sensitive to perturbations, and are accompanied by increased variability of measurements. Following the transition (right), systems again become stable. (B) The stability of neural networks is hypothesized to be reflected in firing irregularity of constituent neurons.
