Mean-driven and fluctuation-driven persistent activity in recurrent networks
Mean-driven and fluctuation-driven persistent activity in recurrent networks
复制标题
DOI：
10.1162/neco.2007.19.1.1
复制
发表时间：
2007-01-01
影响因子：
2.9
通讯作者：
Parga, Nestor
中科院分区：
文献类型：
--
作者：
Renart, Alfonso;Moreno-Bote, Ruben;Parga, Nestor
皮层神经元的脉冲序列表现出高度的不规则性，其脉冲间隔（ISI）分布的变异系数（CV）接近或大于1。有人认为，这种不规则性可能反映了局部皮层回路的特定动态状态，其中兴奋和抑制相互平衡。在这种“平衡”状态下，神经元的平均电流低于阈值，放电由电流波动驱动，导致不规则的泊松样尖峰序列。最近的数据表明，在工作记忆实验的延迟期记录的神经元尖峰序列的不规则程度在几赫兹的低活动状态和几十赫兹的高持续活动状态下是相同的。由于这些持续活动状态之间的差异不可能是由于来自感官输入的外部因素，这表明潜在的网络动力学可能支持不同放电速率下共存的平衡状态。我们使用平均场技术研究了基于电流的泄漏整合与放电（LIF）神经元循环网络中存在多个平衡稳态的可能性。为了评估稳态的平衡程度，我们扩展了现有的平均场理论，使得不仅放电率，而且神经元的峰间间隔分布的变异系数都是自一致的。根据网络的连通性参数，我们找到了不同类型的双稳解。如果局部循环连接主要是兴奋性的，则两种稳定状态的差异主要在于神经元的平均电流。在这种情况下，在升高的持续活动状态下的平均驱动是超过阈值的，并且通常以低尖峰不规则性为特征。如果局部复发性兴奋性和抑制性驱动都很大且接近平衡，甚至以抑制为主，则两种稳定状态共存，都具有阈下电流驱动。在这种情况下，静息状态和助记持久状态下的峰值可变性都很大，但平衡条件意味着参数微调。由于所需的微调程度随着网络规模的增加而增加，另一方面，对于小型网络，传入细胞的电流波动的大小也会增加，总的来说，我们发现，在我们分析的非常简化的模型类型中，波动驱动的持续活动并不是一个稳健的现象。讨论了考虑更现实的模型可能产生的影响。
Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this "balanced" state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.