Mazzucato Luca, Fontanini Alfredo, La Camera Giancarlo
Department of Neurobiology and Behavior and.
Department of Neurobiology and Behavior and Graduate Program in Neuroscience, State University of New York at Stony Brook, Stony Brook, New York 11794
J Neurosci. 2015 May 27;35(21):8214-31. doi: 10.1523/JNEUROSCI.4819-14.2015.
Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model.
对警觉动物群体活动的单试次分析表明，皮层回路动力学通过亚稳态的时间序列演变。亚稳态因其在感觉编码、记忆和决策中的潜在作用而受到研究。然而，对于其产生的网络机制却知之甚少。人们通常认为状态序列的开始是由外部刺激触发的。在这里我们表明，在没有明显感觉刺激的情况下也能观察到状态序列。对警觉大鼠味觉皮层多电极记录的分析揭示了持续的状态序列，其中单个神经元在不同状态下自发地达到几种放电率。这种单神经元多稳定性对现有的发放网络模型构成了挑战，在这些模型中，通常每个神经元最多是双稳态的。我们提出了一个循环发放网络模型，该模型既能解释状态序列的自发产生，又能解释单神经元放电率的多稳定性。每个状态由具有增强的簇内连接的神经簇的激活产生，每个簇中的放电率取决于活跃簇的数量。模拟表明，该模型的群体活动在不同状态之间跳跃，再现了数据中观察到的持续动态。当用外部刺激探测时，该模型预测单神经元多稳定性会淬灭为双稳态，并且逐次试验变异性会降低。这两个预测都在数据中得到了证实。总之，这些结果提供了一个理论框架，在一个单一的机械模型中捕捉了持续的和诱发的网络动态。
