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(B ) The memoryless Bayesian decoder was used to decode the posterior probability of the linearized location from spiking activity and journey-specific place maps (C ) for every time window.
If a Lagrange-based decoder was chosen for the implementation, then the decoding complexity would become infeasible.
Though it made decoding more challenging, the time invariance of the decoder was essential to ensure that any changes of mind we observed were solely due to changing neural activity.
We have summarized this conclusion in the Results (in the subsection headed "Decoding the moment-by-moment population response"): "This decoder was simply a weighted sum of the neurons' smoothed spike counts.
Both methods relied on the leave-one-out procedure that consists in identifying a chunk after the decoder was trained on all chunks but the to-be-decoded one.
The Decoder was meant to be a motivational tool.
The real hand decoder was constructed from MEG signals obtained while moving the intact hand.
The third experiment with the real hand decoder was added to decrease the pain.
The class decoder was trained at the peak classification accuracy of the offline task.
Each character input into the decoder was accompanied by an additional erasure bit.
The random decoder was trained by the MEG signals of the same offline task with randomized types of movements.
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