Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex

Reid, R. Clay; Victor, Jonathan D.; Shapley, Robert

doi:10.1017/s0952523800006350

Cited by 79 publications

(69 citation statements)

References 22 publications

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“…This inhibition is much weaker or even absent in STRFs estimated using grating sequences. The changes in temporal inhibition depend on differences in temporal stimulus statistics and are consistent with previous observations of nonlinear temporal summation (Tolhurst et al, 1980;Mancini et al, 1990;Reid et al, 1992). Grating sequences are temporally white (up to 72 Hz), whereas natural vision movies are biased toward saccade frequencies (3-4 Hz).…”

Section: Natural Vision and Neural Response Propertiessupporting

confidence: 88%

Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons

David¹,

Vinje²,

Gallant³

2004

J. Neurosci.

316

304

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Studies of the primary visual cortex (V1) have produced models that account for neuronal responses to synthetic stimuli such as sinusoidal gratings. Little is known about how these models generalize to activity during natural vision. We recorded neural responses in area V1 of awake macaques to a stimulus with natural spatiotemporal statistics and to a dynamic grating sequence stimulus. We fit nonlinear receptive field models using each of these data sets and compared how well they predicted time-varying responses to a novel natural visual stimulus. On average, the model fit using the natural stimulus predicted natural visual responses more than twice as accurately as the model fit to the synthetic stimulus. The natural vision model produced better predictions in Ͼ75% of the neurons studied. This large difference in predictive power suggests that natural spatiotemporal stimulus statistics activate nonlinear response properties in a different manner than the grating stimulus. To characterize this modulation, we compared the temporal and spatial response properties of the model fits. During natural stimulation, temporal responses often showed a stronger late inhibitory component, indicating an effect of nonlinear temporal summation during natural vision. In addition, spatial tuning underwent complex shifts, primarily in the inhibitory, rather than excitatory, elements of the response profile. These differences in late and spatially tuned inhibition accounted fully for the difference in predictive power between the two models. Both the spatial and temporal statistics of the natural stimulus contributed to the modulatory effects.

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Section: Natural Vision and Neural Response Propertiessupporting

confidence: 88%

Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons

David¹,

Vinje²,

Gallant³

2004

J. Neurosci.

316

304

View full text Add to dashboard Cite

show abstract

“…Could these stimulus-related changes at the perceptual level originate from changes in the properties of single cortical DS cells, or do they simply reflect a population of diverse, but individually fixed, temporal RF profiles? Fixed RFs are consistent with demonstrations that linear spatiotemporal filters account well for response properties including direction selectivity in V1 simple cells (Movshon et al, 1978;Reid et al, 1987;Palmer, 1989, 1994;DeAngelis et al, 1993), but there have been some reports of stimulus-related changes in temporal integration in simple and complex cells in cats and monkeys (Dean et al, 1982;Reid et al, 1992).…”

Section: Introductionsupporting

confidence: 85%

Adaptive Temporal Integration of Motion in Direction-Selective Neurons in Macaque Visual Cortex

Bair¹,

Movshon²

2004

J. Neurosci.

123

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Direction-selective neurons in the primary visual cortex (V1) and the extrastriate motion area MT/V5 constitute a critical channel that links early cortical mechanisms of spatiotemporal integration to downstream signals that underlie motion perception. We studied how temporal integration in direction-selective cells depends on speed, spatial frequency (SF), and contrast using randomly moving sinusoidal gratings and spike-triggered average (STA) analysis. The window of temporal integration revealed by the STAs varied substantially with stimulus parameters, extending farther back in time for slow motion, high SF, and low contrast. At low speeds and high SF, STA peaks were larger, indicating that a single spike often conveyed more information about the stimulus under conditions in which the mean firing rate was very low. The observed trends were similar in V1 and MT and offer a physiological correlate for a large body of psychophysical data on temporal integration. We applied the same visual stimuli to a model of motion detection based on oriented linear filters (a motion energy model) that incorporated an integrate-and-fire mechanism and found that it did not account for the neuronal data. Our results show that cortical motion processing in V1 and in MT is highly nonlinear and stimulus dependent. They cast considerable doubt on the ability of simple oriented filter models to account for the output of direction-selective neurons in a general manner. Finally, they suggest that spike rate tuning functions may miss important aspects of the neural coding of motion for stimulus conditions that evoke low firing rates.

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“…This model should predict as well or better than the Fourier power model. Other nonlinear mechanisms that would likely improve predictions include nonlinear temporal summation (Tolhurst et al 1980;Reid et al 1992), contrast gain control (Wilson & Humanski 1993;Carandini et al 1997) and spatially localized modulation by the non-classical receptive field (Walker et al 1999;Vinje & Gallant 2000). Some nonlinear response properties, such as contrast gain control, are approximated by negative coefficients in the Fourier power STRF, but models that capture divisive and other nonlinear modulation explicitly may prove more accurate.…”

Section: Nonlinear Neurons and Natural Visionmentioning

confidence: 99%

Predicting neuronal responses during natural vision

David

Gallant

2005

Network: Computation in Neural Systems

128

164

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A model that fully describes the response properties of visual neurons must be able to predict their activity during natural vision. While many models have been proposed for the visual system, few have ever been tested against this criterion. To address this issue, we have developed a general framework for fitting and validating nonlinear models of visual neurons using natural visual stimuli. Our approach derives from linear spatiotemporal receptive field (STRF) analysis, which has frequently been used to study the visual system. However, prior to the linear filtering stage typical of STRFs, a linearizing transformation is applied to the stimulus to account for nonlinear response properties. We used this approach to compare two models for neurons in primary visual cortex: a nonlinear Fourier power model, which accounts for spatial phase invariant tuning, and a traditional linear model. We characterized prediction accuracy in terms of the total explainable variance, given intrinsic experimental noise. On average, Fourier power STRFs predicted 40% of explainable variance while linear STRFs were able to predict only 21% of explainable variance. The performance of the Fourier power model provides a benchmark for evaluating more sophisticated models in the future.

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Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex

Cited by 79 publications

References 22 publications

Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons

Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons

Adaptive Temporal Integration of Motion in Direction-Selective Neurons in Macaque Visual Cortex

Predicting neuronal responses during natural vision

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