Prior Expectation Mediates Neural Adaptation to Repeated Sounds in the Auditory Cortex: An MEG Study

Todorović, Ana; Ede, Freek van; Maris, Eric; Lange, Floris P. de

doi:10.1523/jneurosci.1425-11.2011

Cited by 371 publications

(431 citation statements)

References 48 publications

(56 reference statements)

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“…As shown in Figure 7, the predictive currents anticipated the arrival of a second B sound and therefore produced a response to a nonexisting sound, as experimentally observed. Indeed, our results are tightly consistent with MEG and intracranial data obtained on a similar protocol (Hughes et al, 2001;Todorovic et al, 2011).…”

Section: Mmn To Omissionsupporting

confidence: 91%

“…One of the most remarkable properties of the auditory system is that it can generate evoked responses to an absent but expected stimulus (Joutsiniemi and Hari, 1989;Raij et al, 1997;Yabe et al, 1997;Hughes et al, 2001;Todorovic et al, 2011;Wacongne et al, 2011). We similarly tested the response of our network to the omission of an expected sound.…”

Section: Mmn To Omissionmentioning

confidence: 99%

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A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

476

423

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The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA. . . ), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

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Section: Mmn To Omissionsupporting

confidence: 91%

Section: Mmn To Omissionmentioning

confidence: 99%

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

476

423

View full text Add to dashboard Cite

show abstract

“…This decrease is consistent with the body of literature supporting predictive encoding (e.g., Friston, 2009;Mayrhauser et al, 2014;Rao & Ballard, 1999;Summerfield et al, 2008;Todorovic et al, 2011) and suggests that this system could benefit from reduced prediction error in the spatial domain. We conjecture that as the set of possible target locations becomes smaller the prediction error is resolved more quickly and efficiently, which produces less metabolic activation..…”

Section: Location Regularitiessupporting

confidence: 89%

“…Consistent with this view, Bubic et al (2011) found that different neural systems are involved in evaluating predictions regarding object identity, location, and presentation time. Other studies have shown that odd-ball (unexpected) auditory or visual events produce patterns corresponding to surprise or prediction error in respective sensory cortices (Kok et al, 2012;Mustovic et al, 2003;Todorovic, van Ede, Maris, & de Lange, 2011).…”

Section: Introductionmentioning

confidence: 99%

Predictability of what or where reduces brain activity, but a bottleneck occurs when both are predictable

Davis

Hasson

2018

NeuroImage

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Detecting regularities in the sensory environment licenses predictions that enable adaptive behaviour. However, it is unclear whether predictions about object category, location, or both dimensions are mediated by overlapping systems, and relatedly, whether constructing predictions about both category and location is associated with processing bottlenecks. To examine this issue, in an fMRI study, we presented participants with image-series in which non-deterministic transition probabilities enabled predictions about either the location of the next image, its semantic category, both dimensions, or neither (the latter forming a "no-regularity" random baseline condition). Speaking to a common system, all three predictable conditions resulted in reduced BOLD activity in four clusters: left rostral anterior cingulate cortex; bilateral putamen, caudate and thalamus; right precentral gyrus, and left visual cortex. Pointing to a processing bottleneck, in some regions, a significant interaction between the two factors was found whereby category-predictable series were associated with lower activity -but only when location regularity was absent. Finally, category regularity decreased activation in areas of the ventral visual stream and semantic areas of lateral temporal cortex, and location regularity decreased activation in a dorsal fronto-parietal cluster, long implicated in the endogenous control of spatial attention. Our findings confirm and expand a role for dACC/dmPFC and striatum in monitoring or responding to uncertainty in the environment and point to a limited capacity bottleneck when multiple predictions are concurrently licensed.

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“…The former, linked to processes such as local neuronal stimulus specific adaptation (SSA; Ulanovsky et al2003; for a recent review, see Ayala & Malmierca, 2013), requires replication of a sound over time and leads to reduction in early components of the ERP (e.g., 40-60ms; Slabu et al2010). In contrast, expectation suppression is related to expectation of a pattern continuation (Summerfield et al 2008;Alink et al 2010;den Ouden et al 2010;Todorovic et al 2011), with an effect that is present in later ERP components (e.g, 100-200 ms).…”

Section: Discussionmentioning

confidence: 83%