Literature DB >> 30972529

NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods.

Daniel Rasmussen1.   

Abstract

NengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convolutional networks), and then efficiently simulate those models in an easy-to-use, unified framework. In addition, NengoDL allows users to apply deep learning training methods to optimize the parameters of biological neural models. In this paper we present basic usage examples, benchmarking, and details on the key implementation elements of NengoDL. More details can be found at https://www.nengo.ai/nengo-dl.

Keywords:  Computational neuroscience; Deep learning; Nengo; TensorFlow

Mesh:

Year:  2019        PMID: 30972529     DOI: 10.1007/s12021-019-09424-z

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  14 in total

1.  Principles for models of neural information processing.

Authors:  Kendrick N Kay
Journal:  Neuroimage       Date:  2017-08-06       Impact factor: 6.556

2.  A spiking neural model of adaptive arm control.

Authors:  Travis DeWolf; Terrence C Stewart; Jean-Jacques Slotine; Chris Eliasmith
Journal:  Proc Biol Sci       Date:  2016-11-30       Impact factor: 5.349

Review 3.  Using goal-driven deep learning models to understand sensory cortex.

Authors:  Daniel L K Yamins; James J DiCarlo
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

4.  Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator.

Authors:  Jan Gosmann; Chris Eliasmith
Journal:  Front Neuroinform       Date:  2017-05-04       Impact factor: 4.081

5.  Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

Authors:  Nikolaus Kriegeskorte
Journal:  Annu Rev Vis Sci       Date:  2015-11-24       Impact factor: 6.422

6.  Fine-tuning and the stability of recurrent neural networks.

Authors:  David MacNeil; Chris Eliasmith
Journal:  PLoS One       Date:  2011-09-27       Impact factor: 3.240

7.  A unifying mechanistic model of selective attention in spiking neurons.

Authors:  Bruce Bobier; Terrence C Stewart; Chris Eliasmith
Journal:  PLoS Comput Biol       Date:  2014-06-12       Impact factor: 4.475

8.  Training Deep Spiking Neural Networks Using Backpropagation.

Authors:  Jun Haeng Lee; Tobi Delbruck; Michael Pfeiffer
Journal:  Front Neurosci       Date:  2016-11-08       Impact factor: 4.677

9.  A neural model of hierarchical reinforcement learning.

Authors:  Daniel Rasmussen; Aaron Voelker; Chris Eliasmith
Journal:  PLoS One       Date:  2017-07-06       Impact factor: 3.240

10.  Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

Authors:  Bodo Rueckauer; Iulia-Alexandra Lungu; Yuhuang Hu; Michael Pfeiffer; Shih-Chii Liu
Journal:  Front Neurosci       Date:  2017-12-07       Impact factor: 4.677

View more
  5 in total

1.  Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.

Authors:  Alper Yegenoglu; Anand Subramoney; Thorsten Hater; Cristian Jimenez-Romero; Wouter Klijn; Aarón Pérez Martín; Michiel van der Vlag; Michael Herty; Abigail Morrison; Sandra Diaz-Pier
Journal:  Front Comput Neurosci       Date:  2022-05-27       Impact factor: 3.387

2.  Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study.

Authors:  David A Nicholson; Astrid A Prinz
Journal:  J Vis       Date:  2022-06-01       Impact factor: 2.004

3.  Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.

Authors:  Alex Volinski; Yuval Zaidel; Albert Shalumov; Travis DeWolf; Lazar Supic; Elishai Ezra Tsur
Journal:  Patterns (N Y)       Date:  2021-11-18

4.  Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Authors:  Bernhard Vogginger; Felix Kreutz; Javier López-Randulfe; Chen Liu; Robin Dietrich; Hector A Gonzalez; Daniel Scholz; Nico Reeb; Daniel Auge; Julian Hille; Muhammad Arsalan; Florian Mirus; Cyprian Grassmann; Alois Knoll; Christian Mayr
Journal:  Front Neurosci       Date:  2022-04-01       Impact factor: 5.152

Review 5.  Spiking Neural Networks and Their Applications: A Review.

Authors:  Kashu Yamazaki; Viet-Khoa Vo-Ho; Darshan Bulsara; Ngan Le
Journal:  Brain Sci       Date:  2022-06-30
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.