A neurocomputational model of the mismatch negativity.
A neurocomputational model of the mismatch negativity.
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DOI：
10.1371/journal.pcbi.1003288
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发表时间：
2013
影响因子：
4.3
通讯作者：
Friston KJ
中科院分区：
文献类型：
--
作者：
Lieder F;Stephan KE;Daunizeau J;Garrido MI;Friston KJ
失匹配负波（MMN）是由违反规则性诱发的事件相关电位。在这里，我们提出了一个模型的基础上的想法，听觉皮层不断更新的生成模型来预测其感官输入的神经元动力学。MMN，然后建模为由神经元活动报告预测误差诱发的电场的叠加。听觉皮层产生预测和解决预测错误的过程进行了模拟使用广义（贝叶斯）过滤-一种生物学上合理的方案，概率推理的层次动态模型的隐藏状态。由此产生的计划产生现实的MMN波形，解释了定性的影响，偏差的概率和幅度的MMN -在延迟和幅度方面-并作出定量预测偏差的概率和幅度之间的相互作用。这项工作推进了MMN的正式理解，更一般地说，说明了开发经验电磁响应的计算信息动态因果模型的潜力。计算神经成像能够从对潜在机制的脑活动的非侵入性测量中进行定量推断。最终，我们希望不仅从生理学角度，而且从计算角度来理解这些机制。到目前为止，这还没有通过神经成像数据的数学模型来解决（例如，动态因果模型），这些模型更侧重于对生理学的更详细的推断。在这里，我们提出了一个动态的因果模型，解释电生理数据的计算，而不是生理学的第一个实例。具体地说，我们预测的失配负性-一个事件相关的潜力引起的规则性违反-从动态的知觉推理所规定的自由能原理。由此产生的模型解释了不匹配负性的波形和它的一些现象学性质的精度水平，还没有尝试过。这突出了神经计算动态因果模型的潜力，使神经成像数据的神经计算机制的推论。
The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering – a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN – in terms of latency and amplitude – and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and – more generally – illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses. Computational neuroimaging enables quantitative inferences from non-invasive measures of brain activity on the underlying mechanisms. Ultimately, we would like to understand these mechanisms not only in terms of physiology but also in terms of computation. So far, this has not been addressed by mathematical models of neuroimaging data (e.g., dynamic causal models), which have rather focused on ever more detailed inferences about physiology. Here we present the first instance of a dynamic causal model that explains electrophysiological data in terms of computation rather than physiology. Concretely, we predict the mismatch negativity – an event-related potential elicited by regularity violation – from the dynamics of perceptual inference as prescribed by the free energy principle. The resulting model explains the waveform of the mismatch negativity and some of its phenomenological properties at a level of precision that has not been attempted before. This highlights the potential of neurocomputational dynamic causal models to enable inferences from neuroimaging data on neurocomputational mechanisms.
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影响因子：
16.2
作者：
Bastos AM;Usrey WM;Adams RA;Mangun GR;Fries P;Friston KJ
通讯作者：
Friston KJ
影响因子：
5.7
作者：
Garrido, Marta I.;Kilner, James M.;Kiebel, Stefan J.;Stephan, Klaas E.;Baldeweg, Torsten;Friston, Karl J.
通讯作者：
Friston, Karl J.
影响因子：
3
作者：
Grimm, Sabine;Escera, Caries
通讯作者：
Escera, Caries
影响因子：
16.2
作者：
Formisano, E;Kim, DS;Goebel, R
通讯作者：
Goebel, R
影响因子：
1.3
作者：
Baldeweg, Torsten
通讯作者：
Baldeweg, Torsten