scholarly journals The cortical modulation of stimulus-specific adaptation in the auditory midbrain and thalamus: a potential neuronal correlate for predictive coding

2015 ◽  
Vol 9 ◽  
Author(s):  
Manuel S. Malmierca ◽  
Lucy A. Anderson ◽  
Flora M. Antunes
Keyword(s):  
Predictive Coding ◽  
Auditory Midbrain ◽  
Specific Adaptation ◽  
Neuronal Correlate ◽  
Cortical Modulation

Deviance Detection and Encoding Acoustic Regularity in the Auditory Midbrain

2019 ◽  
pp. 706-740 ◽  
Author(s):  
Manuel S. Malmierca ◽  
Guillermo V. Carbajal ◽  
Carles Escera
Keyword(s):  
Coding Theory ◽  
Neuronal Response ◽  
Predictive Coding ◽  
Acoustic Stimulation ◽  
Auditory Midbrain ◽  
Processing Efficiency ◽  
Specific Adaptation ◽  
The Past ◽  
Deviance Detection ◽  
Midbrain Neurons

In the past, there was a rather corticocentric conception of the processing of relationships between sounds that used to mostly relegate the midbrain function to a mere relay. However, increasing neurophysiological evidence demonstrates that the midbrain is, in fact, playing a crucial role in encoding some sorts of regularities present in the flow of acoustic stimulation, adapting the neuronal response for processing efficiency. Midbrain neurons are capable of responding more rapidly and strongly when a new stimulus is not matching to a previously encoded regularity; a phenomenon referred to as deviance detection. This chapter discusses deviance detection evidence in the midbrain, mainly describing the characteristics and mechanisms of stimulus-specific adaptation (SSA), and closing with an interpretation from the standpoint of the predictive coding theory.

Stimulus-specific adaptation, MMN and predictive coding

2021 ◽  
Vol 399 ◽  
pp. 108076
Author(s):  
Manuel S. Malmierca ◽  
Ryszard Auksztulewicz
Keyword(s):  
Predictive Coding ◽  
Specific Adaptation

A Neuronal Correlate of the Precedence Effect Is Associated With Spatial Selectivity in the Barn Owl's Auditory Midbrain

2004 ◽  
Vol 92 (4) ◽  
pp. 2051-2070 ◽  
Author(s):  
Matthew W. Spitzer ◽  
Avinash D. S. Bala ◽  
Terry T. Takahashi
Keyword(s):  
Receptive Field ◽  
Inferior Colliculus ◽  
Sound Localization ◽  
Auditory Processing ◽  
Precedence Effect ◽  
Auditory Midbrain ◽  
Spatial Selectivity ◽  
Neuronal Correlate ◽  
Spatial Tuning ◽  
Underlying Mechanisms

Sound localization in echoic conditions depends on a precedence effect (PE), in which the first arriving sound dominates the perceived location of later reflections. Previous studies have demonstrated neurophysiological correlates of the PE in several species, but the underlying mechanisms remain unknown. The present study documents responses of space-specific neurons in the barn owl's inferior colliculus (IC) to stimuli simulating direct sounds and reflections that overlap in time at the listener's ears. Responses to 100-ms noises with lead-lag delays from 1 to 100 ms were recorded from neurons in the space-mapped subdivisions of IC in anesthetized owls (N2O/isofluorane). Responses to a target located at a unit's best location were usually suppressed by a masker located outside the excitatory portion of the spatial receptive field. The least spatially selective units exhibited temporally symmetric effects, in that the amount of suppression was the same whether the masker led or lagged. Such effects mirror the alteration of localization cues caused by acoustic superposition of leading and lagging sounds. In more spatially selective units, the suppression was often temporally asymmetric, being more pronounced when the masker led. The masker often evoked small changes in spatial tuning that were not related to the magnitude of suppressive effects. The association of temporally asymmetric suppression with spatial selectivity suggests that this property emerges within IC, and not at earlier stages of auditory processing. Asymmetric suppression reduces the ability of highly spatially selective neurons to encode the location of lagging sounds, providing a possible basis for the PE.

ERP Repetition Effects and Mismatch Negativity Generation

2007 ◽  
Vol 21 (3-4) ◽  
pp. 204-213 ◽  
Author(s):  
Torsten Baldeweg
Keyword(s):  
Auditory Cortex ◽  
Perceptual Learning ◽  
Empirical Evidence ◽  
Mismatch Negativity ◽  
Prediction Error ◽  
Predictive Coding ◽  
Repetition Effects ◽  
Specific Adaptation ◽  
Repetition Suppression ◽  
Trace Formation

Neuronal adaptation is a ubiquitous property of the cortex. This review presents evidence from MMN studies that show ERP components with similar adaptive properties. Specifically, I consider the empirical evidence from the perspective of a predictive coding model of perceptual learning and inference. Within this framework, ERP and neuronal repetition effects (repetition suppression) are seen as reductions in prediction error, a process that requires synaptic modifications. Repetition positivity is a human auditory ERP component, which shows similar properties to stimulus-specific adaptation of auditory cortex neurons; a candidate mechanism for auditory trace formation.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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