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@memming | |||||
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Fresh on @bioRxiv. Latent trajectory analysis of MT ensemble spike trains reveals mis-aligned subspaces of stimulus and decision codes. #tweeprint biorxiv.org/content/10.110… Collaboration with Huk lab. @yuanz271 @jcbyts pic.twitter.com/O0grpowbPF
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Il Memming Park
@memming
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10. sij |
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Trial-to-trial variability of single unit spike count in monkey brain area MT is correlated with the animal’s perceptual report in visual motion discrimination tasks [Britten et al. 1996]. Where does this choice-correlation originate from? pic.twitter.com/wqWUvfsgsf
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Il Memming Park
@memming
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Our ensemble analysis revealed that 1-D visual motion is encoded as 1-D subspace at the population level (below). Does the choice-correlation also live solely in this subspace? Maybe it should be, since MT is a “sensory cortex”, and the monkey is nearly “optimal”. pic.twitter.com/RxeQ2MAIr4
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Il Memming Park
@memming
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10. sij |
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But it could also be due to feedback, i.e., noisy temporal integration of sensory evidence can be contributing to the apparent variability in MT. Depending on the alignment between the sensory-coding subspace and the choice-correlation, there are several possibilities: pic.twitter.com/q7QfeYVpTI
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Il Memming Park
@memming
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10. sij |
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Our analysis suggests that a large portion of choice-correlated variability in MT is in a subspace NOT aligned with the sensory encoding and lives in the *null-space*! pic.twitter.com/KfU7sNcHII
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Il Memming Park
@memming
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Furthermore, the choice-correlated variability in the null-space appears much later during the trial, suggesting that this is feedback information from the decision-making process! This information could be useful for learning and adaptation. pic.twitter.com/CT5hROqWpB
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Il Memming Park
@memming
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Methods: all analysis of latent space was done using vLGP dx.doi.org/10.1162/NECO_a…. MT ensemble was well described by 4-D latent space.
(also @yuanz271's brilliant work) pic.twitter.com/3a8wpYYPcs
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