Synchronous firing and higher-order interactions in neuron pool
Synchronous firing and higher-order interactions in neuron pool
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DOI：
10.1162/089976603321043720
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发表时间：
2003-01-01
影响因子：
2.9
通讯作者：
Sakai, Y
中科院分区：
文献类型：
--
作者：
Amari, S;Nakahara, H;Sakai, Y
从信息几何的角度研究了神经元群体同步放电的随机机制。神经元的高阶相互作用，不能减少成对的相关性，被证明存在于同步放电。在每个神经元随机激发的神经元池中，研究活动r的概率分布q（r），其是池中激发神经元的分数。当q（r）具有广泛的分布时，特别是当q（r）具有两个峰值时，神经元在一个时间同步放电，在其他时间静止。产生这种概率分布的机制很有趣，因为当每个神经元独立激发时，由于大数定律，活动r集中在其平均值上。即使存在成对相互作用或三阶相互作用，浓度也无法分辨。这表明高阶相互作用对于产生广泛的活动分布是必要的。我们分析了一个简单的模型，其中神经元接收共同的重叠输入，并证明这样的模型可以有一个广泛的活动分布，产生高阶随机相互作用。
The stochastic mechanism of synchronous firing in a population of neurons is studied from the point of view of information geometry. Higher-order interactions of neurons, which cannot be reduced to pairwise correlations, are proved to exist in synchronous firing. In a neuron pool where each neuron fires stochastically, the probability distribution q(r) of the activity r, which is the fraction of firing neurons in the pool, is studied. When q(r) has a widespread distribution, in particular, when q(r) has two peaks, the neurons fire synchronously at one time and are quiescent at other times. The mechanism of generating such a probability distribution is interesting because the activity r is concentrated on its mean value when each neuron fires independently, because of the law of large numbers. Even when pairwise interactions, or third-order interactions, exist, the concentration is not resolved. This shows that higher-order interactions are necessary to generate widespread activity distributions. We analyze a simple model in which neurons receive common overlapping inputs and prove that such a model can have a widespread distribution of activity, generating higher-order stochastic interactions.