McGraw-Hill's AccessMedicine™

Krampl, Anna; Meeks, Kim

doi:10.1080/15424065.2010.483154

Cited by 2 publications

(2 citation statements)

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“…Indeed, at each stage of the hierarchy, the neuron’s response properties can be modulated by various sensory or non-sensory factors ( Meyer et al, 2014 ). The interactions with these factors may not follow a serial structure ( Kandel, 2013 ). For example, visual areas including V1-V4 and the middle temporal area (MT) also receive top-down feedback from the frontal and temporal lobes ( Noudoost et al, 2010 ; Anderson et al, 2011 ; Gregoriou et al, 2012 ; Gilbert and Li, 2013 ; Kandel, 2013 ; Markov et al, 2014a , b ).…”

Section: Introductionmentioning

confidence: 99%

“…The interactions with these factors may not follow a serial structure ( Kandel, 2013 ). For example, visual areas including V1-V4 and the middle temporal area (MT) also receive top-down feedback from the frontal and temporal lobes ( Noudoost et al, 2010 ; Anderson et al, 2011 ; Gregoriou et al, 2012 ; Gilbert and Li, 2013 ; Kandel, 2013 ; Markov et al, 2014a , b ). These modulations can act at various timescales and in different areas.…”

Section: Introductionmentioning

confidence: 99%

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Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

Weng,

Clark,

Akbarian

et al. 2024

Front. Comput. Neurosci.

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To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors’ contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.

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