Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; Amsterdam Movement Sciences (AMS) & Institute for Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, The Netherlands.
Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
J Neurosci Methods. 2018 Oct 1;308:354-365. doi: 10.1016/j.jneumeth.2018.09.008. Epub 2018 Sep 10.
Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses.
For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best.
The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease.
Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary.
The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.
多个神经元的尖峰脉冲串可以根据总和种群（SP）或标记线（LL）假说进行分析。对外界刺激的反应是由整个神经元群体产生的，或者单个神经元具有自己的编码能力。SPIKE 距离估计可以针对单个、总体尖峰脉冲串或每个神经元分别进行，用于量化这些反应。
对于 SP 情况，我们比较了三种算法，这些算法在所有刺激对中搜索最具区分力的子群体。对于 LL 情况，我们引入了一种新算法，该算法将单独分离不同刺激对的神经元结合在一起。
SP 的最佳方法是对所有可能的子群体进行暴力搜索。然而，它仅适用于小群体。对于更现实的设置，模拟退火明显优于仅增加有限计算负荷的梯度算法。尽管计算简便，我们的新型 LL 方法仍可以处理非常复杂的编码情况。
尖峰脉冲串距离已扩展到分析在 SP 和 LL 编码之间插值的神经群体。这包括参数化区分在不同神经元中发射的尖峰的重要性。然而，这些方法仅考虑整个群体。明确关注子群体使我们的算法具有互补性。
神经群体的编码可能性范围很广。SP 和 LL 情况是我们的算法提供正确识别结果的两个极端。
