Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
5,328
result(s) for
"Neurosciences Mathematics."
Sort by:
Mathematics for neuroscientists
Virtually all scientific problems in Neuroscience require mathematical analysis, and all neuroscientists are increasingly required to have a significant understanding of mathematical methods. There is currently no comprehensive, integrated introductory book on the use of mathematics in Neuroscience, existing books either concentrate solely on theoretical modeling or discuss mathematical concepts for the treatment of very specific problems. This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their importance for neuroscience.
eBook
The Oxford handbook of numerical cognition
This book provides a comprehensive overview of numerical cognition by bringing together writing by leading researchers in psychology, neuroscience, and education, covering work using different methodological approaches in humans and animals. During the last decade there had been an explosion of studies and new findings with theoretical and translational implications. This progress has been made thanks to technological advances enabling sophisticated human neuroimaging techniques and neurophysiological studies of monkeys, and to advances in more traditional psychological and educational research. This has resulted in an enormous advance in our understanding of the neural and cognitive mechanisms of numerical cognition. In addition, there has recently been increasing interest and concern about pupils' mathematical achievement, resulting in attempts to use research to guide mathematics instruction in schools, and to develop interventions for children with mathematical difficulties. This book aims to provide a broad and extensive review of the field of numerical cognition, bringing together work from varied areas. The book covers research on important aspects of numerical cognition, involving findings from the areas of developmental psychology, cognitive psychology, human and animal neuroscience, computational modeling, neuropsychology and rehabilitation, learning disabilities education and individual differences, cross-cultural and cross-linguistic studies, and philosophy. It also includes an overview 'navigator' chapter for each section to provide a brief up-to-date review of the current literature, and to introduce and integrate the topics of the chapters in the section.
eBook
Mathematics Education and Neurosciences: Towards Interdisciplinary Insights into the Development of Young Children's Mathematical Abilities
This chapter contains sections titled:
Introduction
Bidirectional Collaboration
Converging ‘ME’ and ‘NS’
Bridging ‘ME’ with ‘NS’
From ‘MENS’ towards ‘Educational Neuroscience’
Acknowledgements
References
Book Chapter
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.
The authors identify reusable ‘dynamical motifs’ in artificial neural networks. These motifs enable flexible recombination of previously learned capabilities, promoting modular, compositional computation and rapid transfer learning. This discovery sheds light on the fundamental building blocks of intelligent behavior.
Journal Article
Using a Virtual Manipulative Intervention Package to Support Maintenance in Teaching Subtraction with Regrouping to Students with Developmental Disabilities
To live independently, it is critical that students with disabilities maintain the basic mathematical skills they have acquired so they may apply these skills in daily life. To support maintenance of mathematical skills among students with developmental disabilities, the researchers used a multiple probe across participants design to examine the effectiveness of the VRA instructional sequence with fading support in teaching subtraction with regrouping to four students with developmental disabilities. A functional relation was found between the VRA instructional sequence with fading support and students’ accuracy in solving the problems. Students also maintained the skill up to 6 weeks after the intervention.
Journal Article
Multilayer Brain Networks
The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure–function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.
Journal Article
Using a Virtual-Representational-Abstract Integrated Framework to Teach Multiplicative Problem Solving to Middle School Students with Developmental Disabilities
Effective instructional strategies to improve mathematical problem solving skills are critically important to student success in both school-based and real-world mathematics tasks. This study reports effects of a Virtual-Representational-Abstract Integrated framework on the mathematical problem solving skills of three middle school students with developmental disabilities (autism spectrum disorder and intellectual disability). All participants improved in their problem solving accuracy when solving multiplicative comparison word problems using realistic double and triple multipliers. Additionally, all participants maintained their mathematical problem solving accuracy after visual supports (graphic organizer) were removed. Detailed findings and implications for future research and practitioners are discussed.
Journal Article
Differentiating anxiety forms and their role in academic performance from primary to secondary school
Individuals with high levels of mathematics anxiety are more likely to have other forms of anxiety, such as general anxiety and test anxiety, and tend to have some math performance decrement compared to those with low math anxiety. However, it is unclear how the anxiety forms cluster in individuals, or how the presence of other anxiety forms influences the relationship between math anxiety and math performance.
We measured math anxiety, test anxiety, general anxiety and mathematics and reading performance in 1720 UK students (year 4, aged 8-9, and years 7 and 8, aged 11-13). We conducted latent profile analysis of students' anxiety scores in order to examine the developmental change in anxiety profiles, the demographics of each anxiety profile and the relationship between profiles and academic performance.
Anxiety profiles appeared to change in specificity between the two age groups studied. Only in the older students did clusters emerge with specifically elevated general anxiety or academic anxiety (test and math anxiety). Our findings suggest that boys are slightly more likely than girls to have elevated academic anxieties relative to their general anxiety. Year 7/8 students with specifically academic anxiety show lower academic performance than those who also have elevated general anxiety.
There may be a developmental change in the specificity of anxiety and gender seems to play a strong role in determining one's anxiety profile. The anxiety profiles present in our year 7/8 sample, and their relationships with math performance, suggest a bidirectional relationship between math anxiety and math performance.
Journal Article