Adaptation-Induced Plasticity of Orientation Tuning in Adult Visual Cortex

Dragoi, Valentin; Sharma, Jitendra; Sur, Mriganka

doi:10.1016/s0896-6273(00)00103-3

Cited by 467 publications

(585 citation statements)

References 57 publications

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“…Indeed, this is exactly what experiments show [19,21]. When neurons near pinwheel centers are subjected to a pattern adaptation protocol, in which a stimulus of one orientation is presented to the receptive field of a neuron for a period of seconds to minutes, the tuning curve is altered (Fig.…”

Section: Pattern Adaptation-induced Orientation Shifts At Pinwheel Cementioning

confidence: 53%

“…There is now substantial experimental support for some of the aspects of this model ( [24,37,50]; see [25] for a recent review). However, there are also numerous features of orientation tuning that this model cannot explain ( [14,21,44,52]; see [63,65] for a review). Several computational models have proposed alternate schemes, based primarily on recurrent amplification of inputs to cortex, that can largely explain the experimental data without recourse to a strict arrangement of thalamocortical projections required by the feedforward model [7,17,64].…”

Section: Orientation Tuning In Primary Visual Cortexmentioning

confidence: 99%

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Local networks in visual cortex and their influence on neuronal responses and dynamics

Schummers¹,

Mariño²,

Sur³

2004

Journal of Physiology-Paris

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Networks of neurons in the cerebral cortex generate complex outputs that are not simply predicted by their inputs. These emergent responses underlie the function of the cortex. Understanding how cortical networks carry out such transformations requires a description of the responses of individual neurons and of their networks at multiple levels of analysis. We focus on orientation selectivity in primary visual cortex as a model system to understand cortical network computations. Recent experiments in our laboratory and others provide significant insight into how cortical networks generate and maintain orientation selectivity. We first review evidence for the diversity of orientation tuning characteristics in visual cortex. We then describe experiments that combine optical imaging of orientation maps with intracellular and extracellular recordings from individual neurons at known locations in the orientation map. The data indicate that excitatory and inhibitory synaptic inputs are summed across the cortex in a manner that is consistent with simple rules of integration of local inputs. These rules arise from known anatomical projection patterns in visual cortex. We propose that the generation and plasticity of orientation tuning is strongly influenced by local cortical networks-the diversity of these properties arises in part from the diversity of neighbourhood features that derive from the orientation map.

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Section: Pattern Adaptation-induced Orientation Shifts At Pinwheel Cementioning

confidence: 53%

Section: Orientation Tuning In Primary Visual Cortexmentioning

confidence: 99%

Local networks in visual cortex and their influence on neuronal responses and dynamics

Schummers¹,

Mariño²,

Sur³

2004

Journal of Physiology-Paris

Self Cite

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“…The range of A e used in our learning simulations is listed in Table 1, whereas A i was kept at zero. We were able to simulate the adaptation data of Dragoi et al (2000) by decreasing both recurrent excitatory and recurrent inhibitory connections to cells around the adapted orientation by a large fraction, i.e., by setting both A e and A i to large, positive fractional numbers. In addition, unlike the third alternative for the learning simulation mentioned above, A i had to be slightly larger than A e to simulate the adaptation data.…”

Section: Simulating Learning and Adaptation In A Recurrent Modelmentioning

confidence: 99%

“…The possible link between learning and adaptation is strengthened by an earlier psychophysical finding that adaptation, like learning, can also improve orientation discrimination at the adapted orientation, albeit only briefly (Regan and Beverley 1985). However, a recent physiological experiment on anesthetized cats by Dragoi et al (2000) showed that, unlike learning, the V1 orientation tuning curves after adaptation became broader for cells tuned around the adapted orientation. In addition, the peak of these cells' tuning curves shifted away from the adapted orientation.…”

Section: Introductionmentioning

confidence: 98%

“…Here we show that these physiological changes to the orientation tuning after adaptation can also be explained with the recurrent model by altering the connection strengths in a different way. Thus, just as we will compare our learning simulations to the learning study of Schoups et al (2001), we will also compare our adaptation simulations to the adaptation study of Dragoi et al (2000).…”

Section: Introductionmentioning

confidence: 99%

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Learning and Adaptation in a Recurrent Model of V1 Orientation Selectivity

Teich

Niu

2003

Journal of Neurophysiology

145

164

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Learning and adaptation in the domain of orientation processing are among the most studied topics in the literature. However, little effort has been devoted to explaining the diverse array of experimental findings via a physiologically based model. We have started to address this issue in the framework of the recurrent model of V1 orientation selectivity and found that reported changes in V1 orientation tuning curves after learning and adaptation can both be explained with the model. Specifically, the sharpening of orientation tuning curves near the trained orientation after learning can be accounted for by slightly reducing net excitatory connections to cells around the trained orientation, while the broadening and peak shift of the tuning curves after adaptation can be reproduced by appropriately scaling down both excitation and inhibition around the adapted orientation. In addition, we investigated the perceptual consequences of the tuning curve changes induced by learning and adaptation using signal detection theory. We found that in the case of learning, the physiological changes can account for the psychophysical data well. In the case of adaptation, however, there is a clear discrepancy between the psychophysical data from alert human subjects and the physiological data from anesthetized animals. Instead, human adaptation studies can be better accounted for by the learning data from behaving animals. Our work suggests that adaptation in behaving subjects may be viewed as a short-term form of learning.

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Motion Perception

Park

Tadin

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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Motion perception is a key visual modality implicated in a wide range of critical functional roles. In addition to our ability to perceive moving objects, motion processing is involved in guiding locomotion, extracting object shape, figure‐ground segregation, capturing attention, and interpreting actions of our conspecifics. Here, we review advancements in our understanding of visual motion perception. We begin by describing the basic properties of motion, along with the computational challenges underlying detection and integration of motion signals. Next, we review more complex motion processes, discussing global motion perception, higher‐order motion, motion adaptation, motion in three dimensions, and biological motion. An important focus of this chapter is on interactions between motion perception and other sensory and cognitive modalities, including position, learning, attention, awareness, working memory, and multisensory processing. We also review notable examples of atypical motion processing in aging, cortical blindness, akinetopsia, amblyopia, schizophrenia, and autism spectrum disorder. For these topics, we cover key evidence from psychophysics, neurophysiology, neuroimaging, and computational modeling with an aim to provide a better understanding of the mechanisms that underlie our remarkable ability to take advantage of motion signals in the world. Finally, we highlight potentially interesting future directions in motion research.

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Adaptation-Induced Plasticity of Orientation Tuning in Adult Visual Cortex

Cited by 467 publications

References 57 publications

Local networks in visual cortex and their influence on neuronal responses and dynamics

Local networks in visual cortex and their influence on neuronal responses and dynamics

Learning and Adaptation in a Recurrent Model of V1 Orientation Selectivity

Motion Perception

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