The contribution of area MT to visual motion perception depends on training

Liu, Liu D.; Pack, Christopher C.

doi:10.1101/094128

Cited by 2 publications

(2 citation statements)

References 87 publications

(110 reference statements)

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“…Computational work has explained both types of learning based on a process that selects the most informative sensory neurons for a given task (Jacobs, 2009; Bakhtiari, et al, 2020; Lin, et al, 2017). This proposition is entirely consistent with the current results (Figure 4), as well as previous findings in other brain regions (Law & Gold, 2010; Liu & Pack, 2017). Moreover, in accordance with psychophysical results, we find that the selection of informative sensory inputs relies on explicit feedback (Rubin, et al, 2002) in the form of rewards.…”

Section: Discussionsupporting

confidence: 94%

“…Visual learning has traditionally been studied in laboratory tasks that involve extensive training to associate specific stimuli with specific responses. Such training can profoundly alter the underlying cortical circuitry (Chen, et al, 2016; Liu & Pack, 2017; Chowdhury & DeAngelis, 2008) but it does not necessarily reflect the kind of plasticity that occurs in natural settings. We have therefore examined learning in a more naturalistic, free-viewing task that approximates the kind of foraging that primates perform daily in the wild.…”

Section: Discussionmentioning

confidence: 99%

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Long-range cortical synchronization supports abrupt visual learning

Csorba

Krause

Zanos

et al. 2021

Preprint

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Visual cortical plasticity declines sharply after the critical period, and yet we easily learn to recognize new faces and places throughout our lives. Such learning is often characterized by a moment of insight, an abrupt and dramatic improvement in recognition. We studied the brain mechanisms that support this kind of learning, using a behavioral task in which non-human primates rapidly learned to recognize visual images and to associate them with particular responses. Simultaneous recordings from the inferotemporal and prefrontal cortices revealed a transient synchronization of neural activity between these two areas that peaked around the moment of insight. This synchronization was strongest between inferotemporal sites that encoded images and prefrontal sites that encoded rewards. Moreover, its magnitude built up gradually with successive image exposures, suggesting that abrupt learning is the culmination of a search for informative visual signals within a circuit that links sensory information to task demands.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Long-range cortical synchronization supports abrupt visual learning

Csorba

Krause

Zanos

et al. 2021

Preprint

Self Cite

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No cause for pause: new analyses of ramping and stepping dynamics in LIP (Rebuttal to Response to Reply to Comment on Latimer et al 2015)

Latimer

Huk

2017

Preprint

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We recently presented a statistical comparison between two models of latent dynamics in macaque lateral intraparietal (LIP) area spike trains-a continuous 'ramping' (diffusion-to-bound) model, and a discrete 'stepping' model-and found that a substantial fraction of neurons (recorded in two different studies) were better supported by the stepping model . Here, we respond to a recent challenge to the validity of these findings that focuses primarily on the possibility of a lower bound on LIP firing rates . The paper in question proposed alternate formulations of the ramping model, and argued (via indirect analyses) that half the neurons in the population were better explained by the new model; if correct, this would lead to an even split in the number of neurons better explained by each model. These analyses, while interesting, do not alter the conclusions of our original paper. Here, we review the criticisms raised by Zylberberg & Shadlen and report several new analyses using models with lower bounds. First, we show that the stepping model continued to provide a better description of LIP spike trains when fit using only an early period of each trial. Second, we performed a direct model comparison between our stepping model and a ramping-with-baseline model proposed by Zylberberg & Shadlen; we found that (in a pleasing moment of agreement) roughly half the neurons were better explained by each model. Interestingly, inspection of the cells that switched classifications revealed that many did not strictly exhibit the classical ramping PSTHs that motivated these analyses in the first place. We also examined two other issues raised in recent discussions of LIP: (1) We show that a non-integrating model is consistent with some core aspects of behavioral data previously offered as evidence for continuous integration; and (2) We examine analyses based on the response covariance ("CorCE"), and show that it does not reliably distinguish ramping and stepping dynamics for our dataset. Taken together, these discussions highlight the value of data-driven characterizations of both neural and behavioral dynamics with appropriate statistical tools.

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The contribution of area MT to visual motion perception depends on training

Cited by 2 publications

References 87 publications

Long-range cortical synchronization supports abrupt visual learning

Long-range cortical synchronization supports abrupt visual learning

No cause for pause: new analyses of ramping and stepping dynamics in LIP (Rebuttal to Response to Reply to Comment on Latimer et al 2015)

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