Aghagolzadeh Mehdi, Eldawlatly Seif, Oweiss Karim
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA.
IEEE Trans Inf Theory. 2010 Feb 1;56(2):875-899. doi: 10.1109/TIT.2009.2037057.
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.
要理解大脑如何在细胞和群体水平上协调信息处理，一个关键步骤是同时观察介导感知、学习和运动处理的皮层神经元的放电活动。在本文中，我们提出一种信息论方法，以确定神经元之间的协作在编码外部协变量时是否可能构成信息处理的主导机制。具体而言，我们表明，当一个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无法提供最优编码策略。相反，神经元之间的协作可以提供一种“消息传递”机制，在特定的连接结构约束下，该机制能保留协变量中的大部分信息。使用一个具有生物学合理性的统计学习模型，我们展示了所提方法在协同编码一项运动任务时的性能，该运动任务使用从大量神经元中随机抽取的一个子集。与统计独立模型和最大熵（MaxEnt）模型相比，我们证明了其在从有限数据逼近群体联合密度方面的优越性。
