Synergistic Coding by Cortical Neural Ensembles.
Synergistic Coding by Cortical Neural Ensembles.
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DOI：
10.1109/tit.2009.2037057
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发表时间：
2010-02-01
影响因子：
2.5
通讯作者：
Oweiss K
中科院分区：
文献类型：
--
作者：
Aghagolzadeh M;Eldawlatly S;Oweiss K
要了解大脑如何在细胞和群体水平上协调信息处理，关键的一步是同时观察调节感知、学习和运动处理的皮质神经元的放电活动。在这篇文章中，我们提出了一个信息论的方法来确定在编码外部协变量时，神经元之间的合作是否构成了信息处理的控制机制。具体地说，我们证明了当神经元的放电概率取决于与其相连的其他神经元的放电历史时，神经元输出之间的条件独立性可能不会提供最优编码策略。相反，神经元之间的合作可以提供一种“信息传递”机制，在控制协变量连接结构的特定约束下，保留协变量中的大部分信息。使用一个生物学上可信的统计学习模型，我们展示了所提出的方法在协同编码运动任务方面的性能，该方法使用从大量人群中随机抽取的神经元的子集。与统计独立模型和最大熵(MaxEnt)模型相比，我们证明了它在从有限数据中逼近种群的联合密度方面的优越性。
An essential step towards understanding how the brain orchestrates information processing at the cellular and population levels is to simultaneously observe the spiking activity of cortical neurons that mediate perception, learning, and motor processing. In this paper, we formulate an information theoretic approach to determine whether cooperation among neurons may constitute a governing mechanism of information processing when encoding external covariates. Specifically, we show that conditional independence between neuronal outputs may not provide an optimal encoding strategy when the firing probability of a neuron depends on the history of firing of other neurons connected to it. Rather, cooperation among neurons can provide a “message-passing” mechanism that preserves most of the information in the covariates under specific constraints governing their connectivity structure. Using a biologically plausible statistical learning model, we demonstrate the performance of the proposed approach in synergistically encoding a motor task using a subset of neurons drawn randomly from a large population. We demonstrate its superiority in approximating the joint density of the population from limited data compared to a statistically independent model and a maximum entropy (MaxEnt) model.