Search Results (12)

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations. Full article

Background: Although modifiers and function words are critical in cognitive linguistic assessments and cognitive training has proven to promote synaptic neural activity, they often receive limited attention, particularly in computational data-scarce settings. This study addresses communication difficulties associated with cognitive impairments using engineering data, a design to improve the evaluation of language attributes, applied specifically to these elements. A framework was developed to analyze potential language alterations resulting from traumatic brain injury (tbi), using narrative samples, primary data, and unconventional methods to overcome the limitations of existing resources. Methods: The core technique involves pairing language attributes based on defined relationships and assessing responses using standard statistical learning methods. Direct and normalized evaluations of variables, calculated using the Northwestern Narrative Language Analysis (nnla) profile from the original data, serve as benchmarks. The Area Under the Curve (auc) metric with the corresponding statistical support are reported. Results: The results indicate that the proposed method revealed informative patterns involving modifiers and function words that remained hidden in the baseline approaches. Although some exceptions were observed, results showed a substantially consistent behavior, and the responses achieved promote their use in a clinical setting. Conclusions: The findings can provide valuable directions for theoretical and applied research in language assessment. Identifying specific points of breakdown within language structures can improve the accuracy of rehabilitation plans and better leverage the neuroplastic response of the brain for recovery. Full article

Navigation is crucial for animal survival, and despite their small brains, insects are impressive at it. For example, desert ants acquire environmental information by learning to walk before foraging, enabling them to return home accurately over long distances. These learning walks involve multimodal sensory experiences and induce neuroplastic changes in the Central Complex (CX) and the Mushroom Body (MB) of ants’ brains, making them a key topic in behavioural science, neuroscience, and computational modelling. To address unresolved questions in how ants integrate sensory cues and adapt navigation strategies, we propose a computational model that achieves multisensory integration during learning walks. Central to this model is a novel Learning Vector mechanism that dynamically combines visual, olfactory, and path integration inputs to guide movement decisions. Specifically, the agent in our model determines the degree to which it deviates from the homing direction by evaluating the familiarity of the environment. In this way, agents could strike a balance between their tendency to explore and the need to return safely to the nest. Our model replicates key features reported in biological studies and accounts for individual and inter-species variability by tuning parameters such as cue preferences and environmental parameters. This flexibility enables the simulation of species-specific learning walks and supports a unified view of sensory integration and behavioural adaptation. Moreover, it yields testable predictions that may inform future investigations into the neural and behavioural mechanisms underlying insects’ learning walks. How the proposed model could be adapted for robotics navigation has also been discussed. Full article

The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain’s logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations. Full article

Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient’s therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information. Full article

Compared to other primates, humans are late bloomers, with exceptionally long childhood and adolescence. The extensive developmental period of humans is thought to facilitate the learning processes required for the growth and maturation of the complex human brain. During the first two and a half decades of life, the human brain is a construction site, and learning processes direct its shaping through experience-dependent neuroplasticity. Formal and informal learning, which generates long-term and accessible knowledge, is mediated by neuroplasticity to create adaptive structural and functional changes in brain networks. Since experience-dependent neuroplasticity is at full force during school years, it holds a tremendous educational opportunity. In order to fulfill this developmental and learning potential, educational practices should be human-brain-friendly and “ride” the neuroplasticity wave. Neuroscience can inform educators about the natural learning mechanisms of the brain to support student learning. This review takes a neuroscientific lens to explore central concepts in education (e.g., mindset, motivation, meaning-making, and attention) and suggests two methods of using neuroscience as an educational tool: teaching students about their brain (content level) and considering the neuro-mechanisms of learning in educational design (design level). Full article

Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information. Full article

Neuroplasticity is a robust mechanism by which the central nervous system attempts to adapt to a structural or chemical disruption of functional connections between neurons. Mechanical damage from spinal cord injury potentiates via neuroinflammation and can cause aberrant changes in neural circuitry known as maladaptive plasticity. Together, these alterations greatly diminish function and quality of life. This review discusses contemporary efforts to harness neuroplasticity through rehabilitation and neuromodulation to restore function with a focus on motor recovery following cervical spinal cord injury. Background information on the general mechanisms of plasticity and long-term potentiation of the nervous system, most well studied in the learning and memory fields, will be reviewed. Spontaneous plasticity of the nervous system, both maladaptive and during natural recovery following spinal cord injury is outlined to provide a baseline from which rehabilitation builds. Previous research has focused on the impact of descending motor commands in driving spinal plasticity. However, this review focuses on the influence of physical therapy and primary afferent input and interneuron modulation in driving plasticity within the spinal cord. Finally, future directions into previously untargeted primary afferent populations are presented. Full article

The COVID-19 pandemic has had a negative impact on education. The restrictions imposed have undoubtedly led to impairment of the psychological well-being of both teachers and students, and of the way they experience interpersonal relationships. As reported previously in the literature, adverse effects such as loneliness, anxiety, and stress have resulted in a decrease in the cognitive performance of school and higher education students. Therefore, the objective of this work is to present a general overview of the reported adverse effects of the COVID-19 pandemic which may potentially influence the learning performance of students. Some neuroscientific findings related to memory and cognition, such as neuroplasticity and long-term potentiation, are also shown. We also discuss the positive effects of the practice of mindfulness, as well as other simple recommendations based on neuroscientific findings such as restful sleep, physical activity, and nutrition, which can act on memory and cognition. Finally, we propose some practical recommendations on how to achieve more effective student learning in the context of the pandemic. The aim of this review is to provide some assistance in this changing and uncertain situation in which we all find ourselves, and we hope that some of the information could serve as a starting point for hypotheses to be tested in educational research and their association with neuroscience. Full article

Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. Full article

Cognitive impairment is the most common sequelae following an acquired brain injury (ABI) and can have profound impact on the life and rehabilitation potential for the individual. The literature demonstrates that music training results in a musician’s increased cognitive control, attention, and executive functioning when compared to non-musicians. Therapeutic Music Training (TMT) is a music therapy model which uses the learning to play an instrument, specifically the piano, to engage and place demands on cognitive networks in order to remediate and improve these processes following an acquired brain injury. The underlying theory for the efficacy of TMT as a cognitive rehabilitation intervention is grounded in the literature of cognition, neuroplasticity, and of the increased attention and cognitive control of musicians. This single-subject case study is an investigation into the potential cognitive benefit of TMT and can be used to inform a future more rigorous study. The participant was an adult male diagnosed with cognitive impairment as a result of a severe brain injury following an automobile accident. Pre- and post-tests used standardized neuropsychological measures of attention: Trail Making A and B, Digit Symbol, and the Brown– Peterson Task. The treatment period was twelve months. The results of Trail Making Test reveal improved attention with a large decrease in test time on both Trail Making A (−26.88 s) and Trail Making B (−20.33 s) when compared to normative data on Trail Making A (−0.96 s) and Trail Making B (−3.86 s). Digit Symbol results did not reveal any gains and indicated a reduction (−2) in free recall of symbols. The results of the Brown–Peterson Task reveal improved attention with large increases in the correct number of responses in the 18-s delay (+6) and the 36-s delay (+7) when compared with normative data for the 18-s delay (+0.44) and the 36-s delay (−0.1). There is sparse literature regarding music based cognitive rehabilitation and a gap in the literature between experimental research and clinical work. The purpose of this paper is to present the theory for Therapeutic Music Training (TMT) and to provide a pilot case study investigating the potential efficacy of TMT to remediate cognitive impairment following an ABI. Full article

Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate ($\mathrm{\Phi }$) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value $\mathrm{\Phi }$ can change over many generations, and complex tasks require systems with at least a minimum $\mathrm{\Phi }$ . This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to $\mathrm{\Phi }$ due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change $\mathrm{\Phi }$ . One can find arguments to expect one of three possible outcomes: $\mathrm{\Phi }$ might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying $\mathrm{\Phi }$ over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure $\mathrm{\Phi }$ , the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system’s ability to increase $\mathrm{\Phi }$ correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding $\mathrm{\Phi }$ learn better than those that are not. Full article