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首页> 外文期刊>Journal of Computational Neuroscience >The Ising decoder: reading out the activity of large neural ensembles
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The Ising decoder: reading out the activity of large neural ensembles

机译：Ising解码器：读出大型神经团的活动

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The Ising model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.
机译：Ising模型最近对于神经峰值训练数据的统计描述引起了极大关注。在本文中，我们提出并证明了其在构建能够在毫秒时间尺度上预测神经活动模式所代表的刺激的解码器中的用途。在拟合训练数据集之后，可以将Ising解码器“在线”应用以对测试数据进行即时解码。尽管可以使用Boltzmann学习精确拟合这样的模型，但是随着神经集合大小的增加，这种方法在计算上很快变得难以处理。我们展示了几种方法，包括统计物理学的Thouless-Anderson-Palmer（TAP）平均场方法，以及最近开发的最小概率流学习（MPFL）算法，可用于快速推论大型神经网络中的模型参数合奏。与其他问题（例如功能连接性估算）不同，使用Ising模型进行解码需要估算分区功能。由于这涉及所有可能响应的求和，因此此步骤可能会受到限制。平均场方法通过提供分区函数的解析表达式来避免此问题。我们通过将它们应用到来自鼠标视觉皮层模型的模拟神经系综响应中来证明这些解码技术，发现与异构神经调谐和响应特性不同的模型在解码器性能方面的改进。我们的结果证明了使用Ising模型读取或解码由数百个神经元组成的活动的空间模式的实用性。

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