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Multistep inference for generalized linear spiking models curbs runaway excitation

机译：广义线性尖峰模型的多步推断可抑制失控激励

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Generalized linear models (GLMs) are useful tools to capture the characteristic features of spiking neurons; however, the long-term prediction of an autoregressive GLM inferred through maximum likelihood (ML) can be subject to runway self-excitation. We explain here that this runaway excitation is a consequence of the one-step-ahead ML inference used in estimating the parameters of the GLM. Alternatively, inference techniques that incorporate the likelihood of spiking multiple steps ahead in the future can alleviate this instability. We formulate a multi-step log-likelihood (MSLL) as an alternative objective for fitting spiking data. We maximize MSLL to infer an autoregressive GLM for individual spiking neurons recorded from the lateral intraparietal (LIP) area of monkeys during a perceptual decision-making task. While ML inference is shown to produce a GLM with poor fits of the neuron's interspike intervals and autocorrelation, in addition to its runaway excitation, MSLL fit models show a substantial improvement in interval statistics and stable spiking.
机译：广义线性模型（GLM）是捕获尖峰神经元特征的有用工具。但是，通过最大似然（ML）推断的自回归GLM的长期预测可能会受到跑道自激的影响。我们在这里解释说，这种失控的激发是用于估计GLM参数的一步一步ML推理的结果。另外，推断技术结合了将来可能提前采取多个步骤的可能性，可以减轻这种不稳定性。我们制定了多步对数似然（MSLL）作为拟合峰值数据的替代目标。我们最大化MSLL来推断在感知决策任务期间从猴子的侧面顶内（LIP）区域记录的单个尖峰神经元的自回归GLM。虽然ML推理显示出产生的神经元与神经元间突间隔和自相关性的拟合不佳，除了其失控的激发外，MSLL拟合模型显示出间隔统计和稳定尖峰的显着改善。

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