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Visual Cortex and Deep Networks
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried o...
Computational Science – ICCS 2023
The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023. The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks. The theme for 2023, "Computation at the Cutting Edge of Science", highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational and systems biology, environmental systems, finance, and others.
Knowledge Guided Machine Learning
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and d...
SOFSEM 2019: Theory and Practice of Computer Science
This book constitutes the refereed proceedings of the 45th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2019, held in Nový Smokovec, Slovakia, in January 2019. The 34 full papers presented together with 6 invited talks were carefully reviewed and selected from 92 submissions. They presented new research results in the theory and practice of computer science in the each sub-area of SOFSEM 2019: Foundations of theoretical Computer Science, foundations of data science and engineering, and foundations of software engineering.
Computational and Cognitive Neuroscience of Vision
Despite a plethora of scientific literature devoted to vision research and the trend toward integrative research, the borders between disciplines remain a practical difficulty. To address this problem, this book provides a systematic and comprehensive overview of vision from various perspectives, ranging from neuroscience to cognition, and from computational principles to engineering developments. It is written by leading international researchers in the field, with an emphasis on linking multiple disciplines and the impact such synergy can lead to in terms of both scientific breakthroughs and technology innovations. It is aimed at active researchers and interested scientists and engineers in related fields.
SOFSEM 2020: Theory and Practice of Computer Science
This book constitutes the refereed proceedings of the 46th International Conference on Current Trends in Theory and Practice of Informatics, SOFSEM 2020, held in Limassol, Cyprus, in January 2020. The 40 full papers presented together with 17 short papers and 3 invited papers were carefully reviewed and selected from 125 submissions. They presented new research results in the theory and practice of computer science in the each sub-area of SOFSEM 2020: foundations of computer science, foundations of data science and engineering, foundations of software engineering, and foundations of algorithmic computational biology.
Modeling Neural Circuits Made Simple with Python
An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between...
Case Studies in Neural Data Analysis
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary...
Bernard Aubertin
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Machine Learning
Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based appr...
