Stimulus repetition affects both strength and synchrony of macaque inferior temporal cortical activity

Kaliukhovich, Dzmitry A.; Vogels, Rufin

doi:10.1152/jn.00059.2012

Cited by 47 publications

(43 citation statements)

References 60 publications

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“…For future studies on gamma-band synchronization, the present results emphasize the importance of proper condition randomization in the experiment design and of taking repetitions into account in the data analysis. A discussion of related studies (22)(23)(24)(28)(29)(30)(31)(32)(33)(34)(35)(36) is provided in SI Discussion and Fig. S8.…”

Section: Discussionmentioning

confidence: 99%

Stimulus repetition modulates gamma-band synchronization in primate visual cortex

Brunet

Bosman

Vinck

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

120

133

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When a sensory stimulus repeats, neuronal firing rate and functional MRI blood oxygen level-dependent responses typically decline, yet perception and behavioral performance either stay constant or improve. An additional aspect of neuronal activity is neuronal synchronization, which can enhance the impact of neurons onto their postsynaptic targets independent of neuronal firing rates. We show that stimulus repetition leads to profound changes of neuronal gamma-band (∼40-90 Hz) synchronization. Electrocorticographic recordings in two awake macaque monkeys demonstrated that repeated presentations of a visual grating stimulus resulted in a steady increase of visually induced gamma-band activity in area V1, gamma-band synchronization between areas V1 and V4, and gamma-band activity in area V4. Microelectrode recordings in area V4 of two additional monkeys under the same stimulation conditions allowed a direct comparison of firing rates and gamma-band synchronization strengths for multiunit activity (MUA), as well as for isolated single units, sorted into putative pyramidal cells and putative interneurons. MUA and putative interneurons showed repetition-related decreases in firing rate, yet increases in gamma-band synchronization. Putative pyramidal cells showed no repetition-related firing rate change, but a decrease in gamma-band synchronization for weakly stimulus-driven units and constant gamma-band synchronization for strongly driven units. We propose that the repetition-related changes in gamma-band synchronization maintain the interareal stimulus signaling and sharpen the stimulus representation by gamma-synchronized pyramidal cell spikes.adaptation | learning | oscillation | plasticity | priming S timulus repetition typically leads to reduced neuronal firing rates and reduced functional MRI blood oxygen leveldependent signals, whereas behavior that is based on stimulus processing is not affected or is enhanced (1). Different models have been proposed to reconcile these behavioral and neurophysiological findings (1). In a "fatigue model," neuronal responses are reduced in proportion to their amplitude, leaving relative response patterns unchanged; in a "sharpening model," neurons that code features irrelevant to identification of a stimulus exhibit repetition suppression, leading to a sparser and sharpened representation of the repeated stimulus; and in a "facilitation model," stimulus repetition leads to faster stimulus processing, and thereby smaller overall neuronal activity. Gotts and coworkers (2-4) suggested a "synchronization model" in which stimulus repetition leads to reduced firing rates accompanied by increased synchronization. The increased synchronization might explain how less-activated neuronal groups can maintain their impact onto postsynaptic neurons and, ultimately, behavior, while reducing metabolic costs at the same time. The synchronization model has received support from a number of studies in human subjects, using source-localized magnetoencephalography. Ghuman et al. (5) report enhanced ...

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Section: Discussionmentioning

confidence: 99%

Stimulus repetition modulates gamma-band synchronization in primate visual cortex

Brunet

Bosman

Vinck

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

120

133

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“…6,43). Even in IT, responses demonstrate several types of temporal information that meets or exceeds the information provided from a straight "mean rate response" (5,(44)(45)(46)(47). It would be interesting to explore whether additional information can be obtained in a phase code for any of the many features and classes of stimuli that evoke responses in IT.…”

Section: Discussionmentioning

confidence: 99%

Category-selective phase coding in the superior temporal sulcus

Turesson

Logothetis

Hoffman

2012

Proc. Natl. Acad. Sci. U.S.A.

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Object perception and categorization can occur so rapidly that behavioral responses precede or co-occur with the firing rate changes in the object-selective neocortex. Phase coding could, in principle, support rapid representation of object categories, whereby the first spikes evoked by a stimulus would appear at different phases of an oscillation, depending on the object category. To determine whether object-selective regions of the neo-cortex demonstrate phase coding, we presented images of faces and objects to two monkeys while recording local field potentials (LFP) and single unit activity from object-selective regions in the upper bank superior temporal sulcus. Single units showed preferred phases of firing that depended on stimulus category, emerging with the initiation of spiking responses after stimulus onset. Differences in phase of firing were seen below 20 Hz and in the gamma and high-gamma frequency ranges. For all but the <20-Hz cluster, phase differences remained category-specific even when controlling for stimulus-locked activity, revealing that phase-specific firing is not a simple consequence of category-specific differences in the evoked responses of the LFP. In addition, we tested for firing rate-to-phase conversion. Category-specific differences in firing rates accounted for 30-40% of the explained variance in phase occurring at lower frequencies (<20 Hz) during the initial response, but was limited (<20% of the explained variance) in the 30-to 60-Hz frequency range, suggesting that gamma phase-of-firing effects reflect more than evoked LFP and firing rate responses. The present results are consistent with theoretical models of rapid object processing and extend previous observations of phase coding to include object-selective neocortex.category coding | primate | visual perception A rtificial systems have yet to replicate the speed and accuracy of our ability to categorize objects. One challenge has been to understand how the visual system accomplishes such a task, when the speed of recognition (<200 ms) is estimated to accommodate approximately one spike per neuron in a feed-forward "sweep" through the visual system (1-4). Prima facie, such a fast reaction time seems incompatible with the typical ratecoding scheme used to describe neural discrimination of object categories. Given these considerations, the fastest and smallest temporal window for decoding in face/object-selective cells in inferior temporal cortex (IT) would be ∼100-150 ms after stimulus onset. In contrast, rate-based decoding is typically based on windows of 50-500 ms (5, 6), positioned 100 ms after stimulus onset (7,8). Considering that downstream movement planning is still needed, this suggests that some categorization behaviors are occurring during the neural responses thought to underlie them. As a consequence of this temporal bottleneck, several alternative schemes have emerged to accommodate rapid coding in face-and object-selective cells.One of these alternative schemes is phase coding, or phase-offiring coding, invol...

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“…On this view, cells fire at rates that are reduced overall with repetition but are more synchronized in their spike times, permitting better propagation of individual spikes throughout the entire processing pathway in both the feed-forward and feedback directions, facilitating earlier responses. There is supporting evidence for this model, both in single-cell recording studies in animals (e.g., Anderson et al, 2008;Brunet et al, 2014;Kaliukhovich & Vogels, 2012;Hansen & Dragoi, 2011;von Stein, Chiang, & Konig, 2000;Wang et al, 2011) and in humans using MEG (magnetoencephalography; Gilbert et al, 2010;Ghuman et al, 2008) and intracranial EEG (electroencephalography; Engell & McCarthy, 2014). Like the facilitation model, though, the synchrony model requires additional mechanisms to explain the basic formation of representations.…”

Section: Synchrony Modelmentioning

confidence: 93%

Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression

Gotts

2016

Psychon Bull Rev

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Incremental learning models of long-term perceptual and conceptual knowledge hold that neural representations are gradually acquired over many individual experiences via Hebbian-like activity-dependent synaptic plasticity across cortical connections of the brain. In such models, variation in task relevance of information, anatomic constraints, and the statistics of sensory inputs and motor outputs lead to qualitative alterations in the nature of representations that are acquired. Here, the proposal that behavioral repetition priming and neural repetition suppression effects are empirical markers of incremental learning in the cortex is discussed, and research results that both support and challenge this position are reviewed. Discussion is focused on a recent fMRI-adaptation study from our laboratory that shows decoupling of experience-dependent changes in neural tuning, priming, and repetition suppression, with representational changes that appear to work counter to the explicit task demands. Finally, critical experiments that may help to clarify and resolve current challenges are outlined.

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Stimulus repetition affects both strength and synchrony of macaque inferior temporal cortical activity

Cited by 47 publications

References 60 publications

Stimulus repetition modulates gamma-band synchronization in primate visual cortex

Stimulus repetition modulates gamma-band synchronization in primate visual cortex

Category-selective phase coding in the superior temporal sulcus

Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression

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