Training-induced plasticity in the visual cortex of adult rats following visual discrimination learning

Hager, Audrey M.; Dringenberg, Hans C.

doi:10.1101/lm.1787110

Cited by 26 publications

(27 citation statements)

References 36 publications

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“…The stimulus selectivity of visual perceptual learning likely reflects the input specificity of learninginduced synaptic modifications. Interestingly, visual perceptual learning has been reported to potentiate the strength of intracortical synapses, and raise the ceiling for further potentiation of thalamo-cortical synapses in primary visual cortex of binocular rodents (Hager and Dringenberg 2010;Sale et al 2011;Hager et al 2015). Similar stimulus selectivity is observed in the enhancement of VEP amplitudes and single neuron responses following passive visual stimulation in rodents and humans (Frenkel et al 2006;Cooke and Bear 2010;Clapp et al 2012;Montey et al 2013;Kaneko and Stryker 2014).…”

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confidence: 82%

Optimization of visual training for full recovery from severe amblyopia in adults

2016

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The severe amblyopia induced by chronic monocular deprivation is highly resistant to reversal in adulthood. Here we use a rodent model to show that recovery from deprivation amblyopia can be achieved in adults by a two-step sequence, involving enhancement of synaptic plasticity in the visual cortex by dark exposure followed immediately by visual training. The perceptual learning induced by visual training contributes to the recovery of vision and can be optimized to drive full recovery of visual acuity in severely amblyopic adults.

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mentioning

confidence: 82%

Optimization of visual training for full recovery from severe amblyopia in adults

2016

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“…In contrast to traditional assumptions that primary sensory cortical fields function only as stimulus analyzers, associative learning is now known to specifically modify the representations of stimuli in animals and humans in the primary auditory (A1) (Scheich et al, 2011; Weinberger, 2011), somatosensory (S1) (Galvez, Weiss, Weible, & Disterhoft, 2006; Pleger, Blankenburg, Ruff, Driver, & Dolan, 2008), visual (V1) (Hager & Dringenberg, 2010; Miller et al, 2008), olfactory (Li, Howard, Parrish, & Gottfried, 2008) and gustatory (Ifuku, Hirata, Nakamura, & Ogawa, 2003) cortices. Most extensively studied in A1, learning can shift acoustic frequency tuning to strengthen the encoding of sounds that predict reinforcement (Bakin & Weinberger, 1990; Edeline & Weinberger, 1993; Kisley & Gerstein, 2001), which can also produce increased cortical representational area for a tone signal within the tonotopic “map” of A1 (Recanzone, Schreiner, & Merzenich, 1993; Rutkowski & Weinberger, 2005).…”

Section: Introductionmentioning

confidence: 99%

Learning strategy refinement reverses early sensory cortical map expansion but not behavior: Support for a theory of directed cortical substrates of learning and memory

Elias

Bieszczad

Weinberger

2015

Neurobiology of Learning and Memory

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Primary sensory cortical fields develop highly specific associative representational plasticity, notably enlarged area of representation of reinforced signal stimuli within their topographic maps. However, overtraining subjects after they have solved an instrumental task can reduce or eliminate the expansion while the successful behavior remains. As the development of this plasticity depends on the learning strategy used to solve a task, we asked whether the loss of expansion is due to the strategy used during overtraining. Adult male rats were trained in a three-tone auditory discrimination task to bar-press to the CS+ for water reward and refrain from doing so during the CS− tones and silent intertrial intervals; errors were punished by a flashing light and time-out penalty. Groups acquired this task to a criterion within seven training sessions by relying on a strategy that was “bar-press from tone-onset-to-error signal” (“TOTE”). Three groups then received different levels of overtraining: Group ST, none; Group RT, one week; Group OT, three weeks. Post-training mapping of their primary auditory fields (A1) showed that Groups ST and RT had developed significantly expanded representational areas, specifically restricted to the frequency band of the CS+ tone. In contrast, the A1 of Group OT was no different from naïve controls. Analysis of learning strategy revealed this group had shifted strategy to a refinement of TOTE in which they self-terminated bar-presses before making an error (“iTOTE”). Across all animals, the greater the use of iTOTE, the smaller was the representation of the CS+ in A1. Thus, the loss of cortical expansion is attributable to a shift or refinement in strategy. This reversal of expansion was considered in light of a novel theoretical framework (CONCERTO) highlighting four basic principles of brain function that resolve anomalous findings and explaining why even a minor change in strategy would involve concomitant shifts of involved brain sites, including reversal of cortical expansion.

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“…Steady-state visual evoked potentials in visual cortex are selectively amplified for the CS+ following visually cued aversive conditioning in humans (Song and Keil 2014). In rats trained to use visual stimuli to navigate to a hidden platform in a water maze, thalamocortical projections exhibited facilitated long-term potentiation and an increased visual evoked potential was observed in primary visual cortex compared with rats that were exposed to the same visual stimuli when they did not predict the location of the platform (Hager and Dringenberg 2010). In another interesting experiment, rats were fitted with goggles that presented monocular visual cues indicating the effective latency of water reward presentation in an operant task while the activity of single neurons in the deep layers of V1 was recorded (Shuler and Bear 2006).…”

Section: Associative Plasticity In Other Sensory Systemsmentioning

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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Training-induced plasticity in the visual cortex of adult rats following visual discrimination learning

Cited by 26 publications

References 36 publications

Optimization of visual training for full recovery from severe amblyopia in adults

Optimization of visual training for full recovery from severe amblyopia in adults

Learning strategy refinement reverses early sensory cortical map expansion but not behavior: Support for a theory of directed cortical substrates of learning and memory

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

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