Modeling Neural Population Spiking Activity with Gibbs Distributions
Modeling Neural Population Spiking Activity with Gibbs Distributions
复制标题
使用吉布斯分布对神经群体尖峰活动进行建模
DOI：
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复制
发表时间：
2005
期刊：
影响因子：
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通讯作者：
Michael J. Black
中科院分区：
文献类型：
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作者：
F. Wood;S. Roth;Michael J. Black
相关神经群体放电活动的概率建模是理解神经代码和构建实用解码算法的核心。目前不存在用于对多变量相关神经数据进行建模的参数模型，并且数据的高维性质使得完全非参数方法不切实际。为了解决这些问题，我们提出了一种基于能量的模型，其中神经活动的联合概率使用数据的1D边缘直方图的学习函数来表示。使用对比发散和优化过程来学习模型的参数，以找到合适的边缘方向。我们评估的方法使用真实的数据记录的运动皮层神经元的人口。特别是，我们对人口尖峰时间和2D手的位置的联合概率进行建模，并表明我们的模型下的测试数据的可能性显着高于其他模型。这些结果表明，我们的模型捕捉相关的放电活动。我们丰富的神经群体活动的概率模型是朝着测量神经编码中相关性的重要性和改进群体活动的解码迈出的一步。
Probabilistic modeling of correlated neural population firing activity is central to understanding the neural code and building practical decoding algorithms. No parametric models currently exist for modeling multi-variate correlated neural data and the high dimensional nature of the data makes fully non-parametric methods impractical. To address these problems we propose an energy-based model in which the joint probability of neural activity is represented using learned functions of the 1D marginal histograms of the data. The parameters of the model are learned using contrastive divergence and an optimization procedure for finding appropriate marginal directions. We evaluate the method using real data recorded from a population of motor cortical neurons. In particular, we model the joint probability of population spiking times and 2D hand position and show that the likelihood of test data under our model is significantly higher than under other models. These results suggest that our model captures correlations in the firing activity. Our rich probabilistic model of neural population activity is a step towards both measurement of the importance of correlations in neural coding and improved decoding of population activity.
影响因子：
2.5
作者：
Truccolo, W;Eden, UT;Brown, EN
通讯作者：
Brown, EN