- Neural circuits as computational dynamical systemsDavid 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...
- Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networksDavid 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...
- A recurrent neural network for closed-loop intracortical brain-machine interface decodersDavid 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...
- Transferring learning from external to internal weights in echo-state networks with sparse connectivityDavid 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...