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[0] Santhanam G, Linderman MD, Gilja V, Afshar A, Ryu SI, Meng TH, Shenoy KV, HermesB: a continuous neural recording system for freely behaving primates.IEEE Trans Biomed Eng 54:11, 2037-50 (2007 Nov)

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ref: Santhanam-2007.11 tags: HermesB Shenoy continuous neural recording Utah probe flash wireless date: 01-09-2012 00:00 gmt revision:4 [3] [2] [1] [0] [head]

PMID-18018699[0] HermesB: a continuous neural recording system for freely behaving primates.

  • saved the data to compact flash. could record up to 48 hours continuously.
  • recorded from an acceleromter, too - neuron changes were associated with high head accelerations (unsurprisingly).
  • also recorded LFP, and were able to tell with some accuracy what behavioral state the monkey was in.
  • interfaces to the Utah probe
  • not an incredibly small system, judging from the photos.
  • 1600maH battery, 19 hour life @ 2/3 recording duty cycle -> current draw is 120mA, or 450mW.
    • can only record from two channels at once!
    • amplifier gain 610.
    • used ARM microcontroller ADUC2106

____References____

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ref: Kim-2006.06 tags: Hyun Kim Carmena Nicolelis continuous shared control gripper BMI date: 01-06-2012 00:20 gmt revision:2 [1] [0] [head]

IEEE-1634510 (pdf) Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.

  • The pneumatic gripper for picking up objects.
  • 70% brain control, 30% sensor control optimal.
  • Talk about 20Hz nyquist frequency for fast human motor movements, versus the need to smooth and remove noise.
  • Method: proximity sensors
    • collision avoidance 'pain withdrawal'
    • 'infant palmar grasp reflex'
    • Potential field associated with these sensors to implement continuous shared control.
  • Not! online -- used Aurora's data.

____References____

Kim, H.K. and Biggs, J. and Schloerb, W. and Carmena, M. and Lebedev, M.A. and Nicolelis, M.A.L. and Srinivasan, M.A. Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces Biomedical Engineering, IEEE Transactions on 53 6 1164 -1173 (2006)

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ref: work-0 tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics date: 06-30-2009 15:07 gmt revision:0 [head]

http://www.lri.fr/~hansen/cmatutorial.pdf

  • Details a method of sampling + covariance matrix approximation to find the extrema of a continuous (but intractable) fitness function
  • HAs flavors of RLS / Kalman filtering. Indeed, i think that kalman filtering may be a more principled method for optimization?
  • Can be used in high-dimensional optimization problems like finding optimal weights for a neural network.
  • Optimum-seeking is provided by weighting the stochastic samples (generated ala a particle filter or unscented kalman filter) by their fitness.
  • Introductory material is quite good, actually...