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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Code generation: a strategy for neural network simulators.

Dan F M Goodman1

  • 1Laboratoire Psychologie de la Perception, CNRS, Université Paris Descartes, Paris, France. dan.goodman@ens.fr

Neuroinformatics
|September 22, 2010
PubMed
Summary
This summary is machine-generated.

Runtime code generation offers flexibility for neural network simulations, enabling high-level mathematical model specification and C++ performance. This technique optimizes code for various hardware, including GPUs, enhancing simulation capabilities.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Published on: March 2, 2015

Area of Science:

  • Computational Neuroscience
  • Software Engineering
  • High-Performance Computing

Background:

  • Neural network simulations require flexible yet efficient software.
  • Current simulation tools may lack adaptability for diverse mathematical models and hardware.

Purpose of the Study:

  • To introduce a runtime code generation technique for neural network simulation software.
  • To enhance user flexibility in defining mathematical models and optimize performance across different hardware platforms.

Main Methods:

  • Implementing runtime code generation for neural network simulations.
  • Integrating computer algebra systems for code simplification and optimization.
  • Demonstrating the technique with the Brian simulator.

Main Results:

  • Achieved high-level specification of mathematical models with low-level C++ performance.
  • Enabled code reusability across diverse hardware, including Graphics Processing Units (GPUs).
  • Showcased the general applicability of the technique to various simulation packages.

Conclusions:

  • Runtime code generation provides a powerful and flexible approach to neural network simulation.
  • The technique significantly improves performance and hardware compatibility for complex simulations.
  • This method offers a general solution applicable to a wide range of simulation software.