A Study on the educational meaning of math activities connected to mathematics picture books based on the brain-learning principles

Author(s):  
Gyoung Suk Ahn
1999 ◽  
Vol 22 (6) ◽  
pp. 1035-1036
Author(s):  
Friedemann Pulvermüller

True, there may be two language-processing systems, lexicon and syntax. However, could we not say more than that they are computationally and linguistically distinct? Where are they in the brain, why are they where they are, and how can their distinctness and functional properties be explained by biological principles? A brain model of language is necessary to answer these questions. One view is that two different types of corticocortical connections are most important for storing rules and their exceptions: short-range connections within the perisylvian language cortex and long-range connections between this region and other areas. Probabilities of neuroanatomical connections plus associative learning principles explain why different connection bundles specialize in rule storage versus exception learning. Linguistic issues related to language change and plural formation in German are addressed in closing.


2007 ◽  
Vol 2 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Debbie I. Craig

Objective: To present different concepts and techniques related to the application of brain-based learning principles to Athletic Training clinical education. Background: The body of knowledge concerning how our brains physically learn continues to grow. Brain-based learning principles, developed by numerous authors, offer advice on how to facilitate learning in students. Implementing these principles into clinical instruction lessons, whatever the instructional strategy being used, may potentially increase the retention of student knowledge and their ability to transfer that knowledge to different contexts. Description: A review of brain-based learning literature was conducted through searches in Medline, ERIC, SPORTDiscus, and DAI. Common themes from the literature are described. Concepts to use when creating lessons and examples of techniques are then presented to aid the athletic training instructor in implementing some of the brain-based learning principles in clinical education. Examples using different athletic training proficiencies are offered. Application: The profession of athletic training lends itself well to many of the brain-based learning principles. Specifically, the clinical education component of athletic training education is full of possibilities for incorporation of these principles. Many techniques are offered to enhance the athletic training instructor's ability to facilitate student learning through thoughtful incorporation of brain-based learning principles.


2013 ◽  
Vol 3 (2) ◽  
pp. 60-78
Author(s):  
Monika Máčajová

Despite of many worldwide economic problems, every developed society focuses its interest on education. This is undoubtedly caused by the fact that the societies have realized that education is the only way to progress and life quality improvement. Therefore, all educational systems in any period of their development have been making their efforts to seek and find newer approaches to more effective learning and teaching. The present study contributes to the line of works that look for new ways of education through discovering and learning principles of the functioning of the human brain. The paper introduces and explains teaching procedures which respect the needs of the brain. A specific emphasis is put on a) brain activity in various periods; b) evaluation procedures related to the theory of brain‑compatible learning; c) the need to articulate new knowledge and problem solving procedures with respect to optimal stimulation of the brain.


Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1344
Author(s):  
Arjun Magotra ◽  
Juntae Kim

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.


2008 ◽  
Vol 20 (3) ◽  
pp. 709-737 ◽  
Author(s):  
Patrick Byrne ◽  
Suzanna Becker

Numerous single-unit recording studies have found mammalian hippocampal neurons that fire selectively for the animal's location in space, independent of its orientation. The population of such neurons, commonly known as place cells, is thought to maintain an allocentric, or orientation-independent, internal representation of the animal's location in space, as well as mediating long-term storage of spatial memories. The fact that spatial information from the environment must reach the brain via sensory receptors in an inherently egocentric, or viewpoint-dependent, fashion leads to the question of how the brain learns to transform egocentric sensory representations into allocentric ones for long-term memory storage. Additionally, if these long-term memory representations of space are to be useful in guiding motor behavior, then the reverse transformation, from allocentric to egocentric coordinates, must also be learned. We propose that orientation-invariant representations can be learned by neural circuits that follow two learning principles: minimization of reconstruction error and maximization of representational temporal inertia. Two different neural network models are presented that adhere to these learning principles, the first by direct optimization through gradient descent and the second using a more biologically realistic circuit based on the restricted Boltzmann machine (Hinton, 2002; Smolensky, 1986). Both models lead to orientation-invariant representations, with the latter demonstrating place-cell-like responses when trained on a linear track environment.


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