Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.

中文翻译：

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具有多个神经元亚型和突触噪声的随机网络中自发活动的动力学：具有突触噪声的网络中的自发活动。

自发性皮质种群活动表现出多种振荡模式，这些振荡模式通常在慢波睡眠时或在某些麻醉剂下显示出同步性，而在安静的清醒时保持异步。这些皮质状态和它们之间的过渡背后的机制尚未完全了解。在这里，我们研究由Izhikevich方程建模的混合类型的尖峰神经元随机网络中的自发种群活动模式。神经元通过基于电导的突触受到突触噪声的耦合。我们将种群活动模式定位在参数图上，该图由相对抑制突触强度和突触噪声的大小跨越。在没有噪声的情况下，网络会显示瞬时活动模式，无论是振荡的还是恒定的。噪声的作用是将瞬态模式转变为持久模式：对于弱噪声，所有活动模式都是异步非振荡的，与突触强度无关；为了获得更强的噪声，模式具有取决于相对抑制突触强度的振荡和同步特性。在参数空间中，抑制突触强度超过兴奋性突触强度，并且对于中等噪声大小，网络的特征是振荡状态和静态之间的间歇切换，其特性分别类似于同步和异步皮质状态。我们通过将网络状态的现象学全局描述与单个神经元在其局部相空间中的局部描述相结合，来解释这些振荡和静态模式。