Department of Computing, Imperial College London London, UK.
Front Comput Neurosci. 2012 Jan 12;5:62. doi: 10.3389/fncom.2011.00062. eCollection 2011.
The role of gamma frequency oscillation in neuronal interaction, and the relationship between oscillation and information transfer between neurons, has been the focus of much recent research. While the biological mechanisms responsible for gamma oscillation and the properties of resulting networks are well studied, the dynamics of changing phase coherence between oscillating neuronal populations are not well understood. To this end we develop a computational model of competitive selection between multiple stimuli, where the selection and transfer of population-encoded information arises from competition between converging stimuli to entrain a target population of neurons. Oscillation is generated by Pyramidal-Interneuronal Network Gamma through the action of recurrent synaptic connections between a locally connected network of excitatory and inhibitory neurons. Competition between stimuli is driven by differences in coherence of oscillation, while transmission of a single selected stimulus is enabled between generating and receiving neurons via Communication-through-Coherence. We explore the effect of varying synaptic parameters on the competitive transmission of stimuli over different neuron models, and identify a continuous region within the parameter space of the recurrent synaptic loop where inhibition-induced oscillation results in entrainment of target neurons. Within this optimal region we find that competition between stimuli of equal coherence results in model output that alternates between representation of the stimuli, in a manner strongly resembling well-known biological phenomena resulting from competitive stimulus selection such as binocular rivalry.
伽马频率振荡在神经元相互作用中的作用，以及振荡与神经元之间信息传递的关系，是最近许多研究的焦点。虽然负责伽马振荡的生物机制和由此产生的网络特性已经得到很好的研究，但振荡神经元群体之间相位相干性变化的动力学还不太清楚。为此，我们开发了一个用于多个刺激之间竞争选择的计算模型，其中群体编码信息的选择和传递是由会聚刺激之间的竞争产生的，以调节目标神经元群体。振荡是通过兴奋和抑制神经元局部连接网络之间的递归突触连接产生的，通过 Pyramidal-Interneuronal Network Gamma 产生。刺激之间的竞争是由振荡的相干性差异驱动的，而通过通信-通过-相干性，在生成和接收神经元之间可以实现单个选定刺激的传输。我们探索了不同神经元模型中突触参数变化对刺激竞争传输的影响，并在递归突触环的参数空间中确定了一个连续区域，其中抑制诱导的振荡导致目标神经元的同步。在这个最优区域内，我们发现具有相同相干性的刺激之间的竞争导致模型输出交替表示刺激，这种方式与竞争刺激选择导致的众所周知的生物现象非常相似，例如双眼竞争。
