Auditory map plasticity: diversity in causes and consequences

Schreiner, Christoph E.; Polley, Daniel B.

doi:10.1016/j.conb.2013.11.009

Cited by 97 publications

(79 citation statements)

References 148 publications

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“…A distributed code posits subtle changes in a whole population of neurons, with the functional benefits appearing only at the population level. For audition, perceptual learning has indeed been observed through distributed changes in ''tonotopic'' frequency maps, using pure tones [12,13]. As noise has a flat spectrum on average, a distributed code could not rely on tonotopy, but it could possibly recruit more-complex timbre maps [14,15].…”

Section: Resultsmentioning

confidence: 99%

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

et al. 2015

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Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.

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Section: Resultsmentioning

confidence: 99%

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

et al. 2015

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“…This debate has evolved along with our changing understanding of the auditory cortex's function, initially emphasizing changes in frequency coding (Bakin and Weinberger 1990), expanding to emphasize spectrotemporal and spatial elements of the auditory stimuli (Polley et al 2006;Froemke et al 2013;Zhang et al 2013;Schreiner and Polley 2014), and more recently emphasizing more cognitive models of cortical function like categorization of stimuli (Scheich et al 2011) and dynamic, task-dependent reshaping of representations Yin et al 2014). The more recent observations of learning-related plasticity in the olfactory, gustatory, and somatosensory systems have similarly been interpreted in light of the current understanding of each sensory structure's purpose.…”

Section: Discussionmentioning

confidence: 99%

“…This inspired an extensive series of investigations of learninginduced plasticity in the inferior colliculus, auditory thalamus, and auditory cortices (Ohl and Scheich 2005;Scheich et al 2011;Schreiner and Polley 2014;Weinberger 2015). In the auditory system, most individual neurons have receptive fields tuned to a single "best frequency" to which they are maximally sensitive and most strongly responsive.…”

Section: Associative Plasticity In the Auditory Systemmentioning

confidence: 99%

Associative learning and sensory neuroplasticity: how does it happen and what is it good for?

McGann

2015

Learn. Mem.

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Historically, the body's sensory systems have been presumed to provide the brain with raw information about the external environment, which the brain must interpret to select a behavioral response. Consequently, studies of the neurobiology of learning and memory have focused on circuitry that interfaces between sensory inputs and behavioral outputs, such as the amygdala and cerebellum. However, evidence is accumulating that some forms of learning can in fact drive stimulus-specific changes very early in sensory systems, including not only primary sensory cortices but also precortical structures and even the peripheral sensory organs themselves. This review synthesizes evidence across sensory modalities to report emerging themes, including the systems' flexibility to emphasize different aspects of a sensory stimulus depending on its predictive features and ability of different forms of learning to produce similar plasticity in sensory structures. Potential functions of this learning-induced neuroplasticity are discussed in relation to the challenges faced by sensory systems in changing environments, and evidence for absolute changes in sensory ability is considered. We also emphasize that this plasticity may serve important nonsensory functions, including balancing metabolic load, regulating attentional focus, and facilitating downstream neuroplasticity.

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“…Plasticity of the auditory cortex leads to manifold changes over time (26)(27)(28). Thus, it is a limitation of our study measuring only pre surgery and 3 days postsurgery.…”

Section: Discussionmentioning

confidence: 96%