PMID-15247483[0] Cognitive control signals for Neural Prosthetics
- decode intended target from 200 to 1100ms of memory period (reward on correct, etc).
- got good success rates with relatively few neurons (like 8 for 8 targets) -- yet decode rates were not that good, not at all as good as Fetz or Schmidt.
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- used pareital reach region (PRR), a subsection of posterior partietal cortex PPC, which represents the goals of the reach in visual coordinates. In the experiment, the implanted in media intrapareital (MIP)
- in encodes the intended goal rather than the trajectory to achieve that goal.
- PMd also seems to encode planning activity, though less is known about that.
- used an adaptive database to map neuronal activity to targets; eventually, the database contained only (correct) brain-control trials.
- neuronal responses were recorded from parietal reach region (PRR) with 64 microwire electrodes in 4 monkeys, plus 32 microwire electrodes in PMd
- monkeys were tained to fixate on the center of the screen dring the task, though free fixation was also tested and seemed to work ok.
- monkeys had to press cue, fixate, observe target location, wait ~2 sec, and move to the (remembered) target location when cue disappeared.
- they use a static or continually updated 'database' for predicting which of four targets the monkey wants to go to during the instructed delay task.
- able to predict with moderate accuracy the expected value of the target as well as its (discrete) position.
- predictions were made during the delay period while there was no motor movement.
- predictions worked equally well for updated and static databases.
- monkeys were able to increase their performance on the BMI trials over the course of training.
- reward type or size modulated the tuning of BMI neurons in the ecpected way, though aversive stimuli did not increase the tuning - suggesting that the tuning is not a function of attention (maybe).
- the database consisted of 900ms of spike recordings starting 200ms after cue for 30 reach trials for each target. spike trains were projected onto Haar wavelets (sorta like a binary tree), and the filter coefficients were used to describe P(r), the probability of response, and P(r|s), probability of response given the target. then they used bayes rule (P(r) and P(r|s) were approximated with histograms, i think) to find P(s|r) - a discrete function - which it is easy to find the maximum of.
- adding more trials offline improved the decode performance.
- supporting online material.
PMID-15491902 Cognitive neural prosthetics
- LFPs are easier to record and may last longer (but they are not as 'sexy').
- suggest future electrodes will move automatically, peizo-drive perhaps.
- PRR receives direct visual projections & codes for reaches in visual coordinates relative to the current direction of gaze.
- PRR can hold the plan for a movement in short-term memory.
- 16 neurons peak..?
- In area LIP of PPC Platt and Glimcher PMID-10421364 found cells that code the expected value of rewards.
- 20Hz beta-band oscillation indicated the behavioral state of the animal. While planning for a saccade it slowly increased, whereas at the time of movement in dramatically increased in amplitude.
- LFP was better than spikes for a state decode.
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