Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models

@inproceedings{Yu2007NeuralDO,
  title={Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models},
  author={Byron M. Yu and John P. Cunningham and Krishna V. Shenoy and Maneesh Sahani},
  booktitle={ICONIP},
  year={2007}
}
To date, the neural decoding of time-evolving physical state – for example, the path of a foraging rat or arm movements – has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the movements using nonlinear trajectory models, thereby yielding more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an important… CONTINUE READING
13 Citations
37 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 37 references

Similar Papers

Loading similar papers…