David Sussillo

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. ncbi request reprint Neural circuits as computational dynamical systems
    David Sussillo
    Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, CA 94305, United States Electronic address
    Curr Opin Neurobiol 25:156-63. 2014
  2. doi request reprint Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
    David Sussillo
    Department of Electrical Engineering, Neurosciences Program, Stanford University, Stanford, CA 94305 9505, USA
    Neural Comput 25:626-49. 2013
  3. pmc A recurrent neural network for closed-loop intracortical brain-machine interface decoders
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 9505, USA
    J Neural Eng 9:026027. 2012
  4. pmc Transferring learning from external to internal weights in echo-state networks with sparse connectivity
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, California, United States of America
    PLoS ONE 7:e37372. 2012

Collaborators

Detail Information

Publications4

  1. ncbi request reprint Neural circuits as computational dynamical systems
    David Sussillo
    Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, CA 94305, United States Electronic address
    Curr Opin Neurobiol 25:156-63. 2014
    ..I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex...
  2. doi request reprint Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
    David Sussillo
    Department of Electrical Engineering, Neurosciences Program, Stanford University, Stanford, CA 94305 9505, USA
    Neural Comput 25:626-49. 2013
    ..In all cases, the mechanisms of trained networks could be inferred from the sets of fixed and slow points and the linearized dynamics around them...
  3. pmc A recurrent neural network for closed-loop intracortical brain-machine interface decoders
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 9505, USA
    J Neural Eng 9:026027. 2012
    ..Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications...
  4. pmc Transferring learning from external to internal weights in echo-state networks with sparse connectivity
    David Sussillo
    Department of Electrical Engineering, Stanford University, Stanford, California, United States of America
    PLoS ONE 7:e37372. 2012
    ..Through an analysis of the conditions required to make transfer of learning work, we define the concept of a "self-sensing" network state, and we compare and contrast this with compressed sensing...