Pacemaker and network mechanisms of neural rhythm generation (original) (raw)

The origin of flexible rhythmic activity in brain circuits, or even in smaller neural networks, like central pattern generator (CPG) motor networks, is still not fully understood. The main unresolved questions are (i) what are the respective roles of intrinsic neuronal rhythms and network based dynamics in systems of coupled, heterogeneous, intrinsically complex, even chaotic, neurons? and (ii) what mechanisms are underlying the coexistence of robustness and flexibility in the observed rhythmic spatio-temporal patterns? One common view is that particular neurons provide the rhythmogenic component while the connections between different neurons are responsible for the regularization and synchronization of groups of neurons and for specific phase relationships in multi-phasic bursting patterns. The opposing view is that constituent neurons are by themselves nonrhythmogenic and the emergence of rhythmic bursting behaviors is entirely due to the network interactions. The reality is more interesting and challenging, especially, when we are concerned about the brain. Usually, different mechanisms of rhythm generation coexist in the brain and rhythms from different sources and different levels of integration interact closely. It is important to emphasize that fast rhythms and slow rhythms need different levels of abstraction for describing and understanding them. It is reasonable to consider three such levels: (i) the neuronal level, (ii) the neuronal module or neuronal mass level, and (iii) the mental mode level. The analysis of low frequency (< 0.1 Hz) oscillations, for example, needs coarse-grained models of the interaction of mental modes, i.e., perceptional, cognitive and emotional modes. The chapter is organized in the following way. In the first part (neuronal level) we describe the results of computer simulations examining how spatio-temporal rhythmic patterns emerge in motif networks of Hodgkin-Huxley (H-H) neurons connected by slow inhibitory synapses with a non-symmetric pattern of coupling strengths. We demonstrate that the interplay between intrinsic and network dynamics can lead to either cooperation or competition, depending on three basic control parameters identified in the models: (i) the shape of intrinsic bursts, (ii) the strength of the coupling between neurons and (iii) the degree of asymmetry in the connectivity matrix. The cooperation of intrinsic dynamics and network mechanisms is shown to correlate with bistability, i.e., the coexistence of two different attractors in the phase space of the system