10 resultados para categorization IT PFC computational neuroscience model HMAX

em Digital Peer Publishing


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ModelDB's mission is to link computational models and publications, supporting the field of computational neuroscience (CNS) by making model source code readily available. It is continually expanding, and currently contains source code for more than 300 models that cover more than 41 topics. Investigators, educators, and students can use it to obtain working models that reproduce published results and can be modified to test for new domains of applicability. Users can browse ModelDB to survey the field of computational neuroscience, or pursue more focused explorations of specific topics. Here we describe tutorials and initial experiences with ModelDB as an interactive educational tool.

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This document corresponds to the tutorial on realistic neural modeling given by David Beeman at WAM-BAMM*05, the first annual meeting of the World Association of Modelers (WAM) Biologically Accurate Modeling Meeting (BAMM) on March 31, 2005 in San Antonio, TX. Part I - Introduction to Realistic Neural Modeling for the Beginner: This is a general overview and introduction to compartmental cell modeling and realistic network simulation for the beginner. Although examples are drawn from GENESIS simulations, the tutorial emphasizes the general modeling approach, rather than the details of using any particular simulator. Part II - Getting Started with Modeling Using GENESIS: This builds upon the background of Part I to describe some details of how this approach is used to construct cell and network simulations in GENESIS. It serves as an introduction and roadmap to the extended hands-on GENESIS Modeling Tutorial.

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This tutorial is intended to be a "quick start" to creating simulations with GENESIS. It should give you the tools and enough information to let you quickly begin creating cells and networks with GENESIS, making use of the provided example simulations. Advanced topics are covered by appropriate links to the Advanced Tutorials on Realistic Neural Modeling.

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This tutorial gives a step by step explanation of how one uses experimental data to construct a biologically realistic multicompartmental model. Special emphasis is given on the many ways that this process can be imprecise. The tutorial is intended for both experimentalists who want to get into computer modeling and for computer scientists who use abstract neural network models but are curious about biological realistic modeling. The tutorial is not dependent on the use of a specific simulation engine, but rather covers the kind of data needed for constructing a model, how they are used, and potential pitfalls in the process.

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PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

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What does it mean for curriculum to be interactive? It encourages student engagement and active participation in both individual and group work. It offers teachers a coherent set of materials to choose from that can enhance their classes. It is the product of on-going development and continuous improvement based on research and feedback from the field. This paper will introduce work in progress from the Center for Excellence in Education, Science, and Technology (CELEST), an NSF Science of Learning Center. Among its many goals, CELEST is developing a unique educational curriculum, an interactive curriculum based upon models of mind and brain. Teachers, administrators, and governments are naturally concerned with how students learn. Students are greatly concerned about how minds work, including how to learn. CELEST aims to introduce curricula that not only meet current U.S. standards in mathematics, science, and psychology but also influence plans to improve those standards. Software and support materials are in development and available at http://cns.bu.edu/celest/private/. Interested parties are invited to contact the author for access.

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One of the main roles of the Neural Open Markup Language, NeuroML, is to facilitate cooperation in building, simulating, testing and publishing models of channels, neurons and networks of neurons. MorphML, which was developed as a common format for exchange of neural morphology data, is distributed as part of NeuroML but can be used as a stand-alone application. In this collection of tutorials and workshop summary, we provide an overview of these XML schemas and provide examples of their use in down-stream applications. We also summarize plans for the further development of XML specifications for modeling channels, channel distributions, and network connectivity.

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The IDA model of cognition is a fully integrated artificial cognitive system reaching across the full spectrum of cognition, from low-level perception/action to high-level reasoning. Extensively based on empirical data, it accurately reflects the full range of cognitive processes found in natural cognitive systems. As a source of plausible explanations for very many cognitive processes, the IDA model provides an ideal tool to think with about how minds work. This online tutorial offers a reasonably full account of the IDA conceptual model, including background material. It also provides a high-level account of the underlying computational “mechanisms of mind” that constitute the IDA computational model.