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Brain Sciences
Special Issues
Perceptual Learning and Cortical Plasticity
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Perceptual Learning and Cortical Plasticity

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Behavioral Neuroscience".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1090

Special Issue Editor

Dr. Mario Trevino

E-Mail Website
Guest Editor
Laboratory of Cortical Plasticity and Perceptual Learning, Institute of Neurosciences, University of Guadalajara, Guadalajara 44910, Jalisco, Mexico
Interests: neuroscience; synaptic plasticity; visual learning

Special Issue Information

Dear Colleagues,

Perceptual learning, the process through which experience refines sensory processing, is essential for our adaptation to dynamic environmental demands. This Special Issue on Perceptual Learning and Cortical Plasticity seeks to bring together a diverse array of contributions that investigate the mechanisms driving cortical plasticity and their critical role in perceptual learning. By incorporating a variety of perspectives, we aim to advance our understanding of how experience-driven changes influence neural function across cortical regions and sensory modalities.

We welcome submissions that examine the cellular, synaptic, and circuit-level foundations of plasticity, including studies of synaptic remodeling such as strengthening, weakening, and reorganization in response to perceptual tasks. Contributions may include research on structural and functional plasticity revealed through advanced methodologies such as imaging, electrophysiology, or molecular analyses. Comparative studies across model organisms that provide insights into adaptations in sensory learning, as well as multimodal approaches integrating neurophysiological, behavioral, and computational techniques, are encouraged. Additionally, we invite investigations into differences between developmental and adult plasticity, focusing on critical period dynamics and the brain’s capacity for lifelong adaptation.

We aim to emphasize the wider significance of perceptual learning and cortical plasticity by encouraging studies that explore their applications, including neural network models, rehabilitation strategies, and interventions to enhance sensory and cognitive functions. Additionally, we welcome research exploring the influence of neuromodulation and sensory context on plasticity, as well as studies that integrate theoretical and experimental approaches to provide deeper insights into these mechanisms.

Dr. Mario Trevino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • perceptual learning
  • cortical plasticity
  • synaptic plasticity
  • neural network models
  • sensory modalities
  • neurophysiology
  • structural plasticity
  • behavioral neuroscience
  • rehabilitation neuroscience
  • experience-dependent plasticity

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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22 pages, 3487 KiB  
Article
Voice-Evoked Color Prediction Using Deep Neural Networks in Sound–Color Synesthesia
by Raminta Bartulienė, Aušra Saudargienė, Karolina Reinytė, Gustavas Davidavičius, Rūta Davidavičienė, Šarūnas Ašmantas, Gailius Raškinis and Saulius Šatkauskas
Brain Sci. 2025, 15(5), 520; https://doi.org/10.3390/brainsci15050520 - 19 May 2025
Viewed by 238
Abstract
Background/Objectives: Synesthesia is an unusual neurological condition when stimulation of one sensory modality automatically triggers an additional sensory sensation in an additional unstimulated modality. In this study, we investigated a case of sound–color synesthesia in a female with impaired vision. After confirming a [...] Read more.
Background/Objectives: Synesthesia is an unusual neurological condition when stimulation of one sensory modality automatically triggers an additional sensory sensation in an additional unstimulated modality. In this study, we investigated a case of sound–color synesthesia in a female with impaired vision. After confirming a positive case of synesthesia, we aimed to determine the sound features that played a key role in the subject’s sound perception and color development. Methods: We applied deep neural networks and a benchmark of binary logistic regression to classify blue and pink synesthetically voice-evoked color classes using 136 voice features extracted from eight study participants’ voice recordings. Results: The minimum Redundancy Maximum Relevance algorithm was applied to select the 20 most relevant voice features. The recognition accuracy of 0.81 was already achieved using five features, and the best results were obtained utilizing the seventeen most informative features. The deep neural network classified previously unseen voice recordings with 0.84 accuracy, 0.81 specificity, 0.86 sensitivity, and 0.85 and 0.81 F1-scores for blue and pink classes, respectively. The machine learning algorithms revealed that voice parameters, such as Mel-frequency cepstral coefficients, Chroma vectors, and sound energy, play the most significant role. Conclusions: Our results suggest that a person’s voice’s pitch, tone, and energy affect different color perceptions. Full article
(This article belongs to the Special Issue Perceptual Learning and Cortical Plasticity)
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24 pages, 4969 KiB  
Article
Adrenergic Modulation of Cortical Gain and Sensory Processing in the Mouse Visual Cortex
by Ricardo Medina-Coss y León, Elí Lezama, Inmaculada Márquez and Mario Treviño
Brain Sci. 2025, 15(4), 406; https://doi.org/10.3390/brainsci15040406 - 17 Apr 2025
Viewed by 484
Abstract
Background/Objectives: Sensory perception is influenced by internal neuronal variability and external noise. Neuromodulators such as norepinephrine (NE) regulate this variability by modulating excitation–inhibition balance, oscillatory dynamics, and interlaminar connectivity. While NE is known to modulate cortical gain, it remains unclear how it shapes [...] Read more.
Background/Objectives: Sensory perception is influenced by internal neuronal variability and external noise. Neuromodulators such as norepinephrine (NE) regulate this variability by modulating excitation–inhibition balance, oscillatory dynamics, and interlaminar connectivity. While NE is known to modulate cortical gain, it remains unclear how it shapes sensory processing under noisy conditions. This study investigates how adrenergic modulation affects signal-to-noise processing and perceptual decision-making in the primary visual cortex (V1) of mice exposed to varying levels of visual noise. Methods: We performed in vivo local field potential (LFP) recordings from layers 2/3 and 4 of V1 in sedated mice to assess the impact of visual noise and systemic administration of atomoxetine, a NE reuptake inhibitor, on cortical signal processing. In a separate group of freely moving mice, we used a two-alternative forced-choice to evaluate the behavioral effects of systemic and intracortical adrenergic manipulations on visual discrimination. Results: Moderate visual noise enhanced cortical signal processing and visual choices, consistent with stochastic resonance. High noise levels impaired both. Systemic atomoxetine administration flattened the cortical signal-to-noise ratio function, suggesting disrupted gain control. Behaviorally, clonidine impaired accuracy at moderate noise levels, while atomoxetine reduced discrimination performance and increased response variability. Intracortical NE infusions produced similar effects. Conclusions: Our findings demonstrate that NE regulates the balance between signal amplification and noise suppression in a noise- and context-dependent manner. These results extend existing models of neuromodulatory function by linking interlaminar communication and cortical variability to perceptual decision-making. Full article
(This article belongs to the Special Issue Perceptual Learning and Cortical Plasticity)
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