Neural Dynamics
Gregor Schöner, Hendrik Reimann, Jonas Lins · Oxford University Press eBooks · 2015
This chapter introduces the core concepts of neural dynamics on which dynamic field theory (DFT) is based. Behavior is generated by the central nervous system (CNS). From this observation, the argument is made that the inner state of the CNS must be described by continuous variables that evolve continuously over time. The concept of activation is introduced to characterize the inner state of the CNS; activation evolves over time as described by a dynamical system. The form this neural dynamics takes is based on the need for states of the CNS to be stable—for behaviorally significant states to resist perturbations. Stability means that the system coheres around special states called attractors where the rate of change of activation is balanced. This might occur under the influence of sensory input, or when neurons are coupled together, passing excitatory or inhibitory activation back and forth. Also discussed is the movement of the CNS into and out of attractor states, which are formally called instabilities. Such instabilities arise in the CNS due to the nonlinear way in which neurons interact.