Cortical Sensitivity to Visual Features in Natural Scenes

Felsen, Gidon; Touryan, Jon; Han, Feng; Dan, Yang

doi:10.1371/journal.pbio.0030342

Cited by 147 publications

(190 citation statements)

References 54 publications

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“…Studies in retina (Smirnakis et al, 1997) and primary visual cortex (Mechler et al, 2002;Felsen et al, 2005) suggest the existence of nonlinear mechanisms sensitive to structure in the spatial phase spectrum, e.g., as associated with edges or more complex features. Our white noise stimuli are not ideal to test for such mechanisms because they have a flat amplitude spectrum and a random phase spectrum and therefore do not allow investigation of the effects of second-order spatial statistics.…”

Section: Discussionmentioning

confidence: 99%

The Statistical Computation Underlying Contrast Gain Control

Bonin

Mante

Carandini

2006

Journal of Neuroscience

107

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In the early visual system, a contrast gain control mechanism sets the gain of responses based on the locally prevalent contrast. The measure of contrast used by this adaptation mechanism is commonly assumed to be the standard deviation of light intensities relative to the mean (root-mean-square contrast). A number of alternatives, however, are possible. For example, the measure of contrast might depend on the absolute deviations relative to the mean, or on the prevalence of the darkest or lightest intensities. We investigated the statistical computation underlying this measure of contrast in the cat's lateral geniculate nucleus, which relays signals from retina to cortex. Borrowing a method from psychophysics, we recorded responses to white noise stimuli whose distribution of intensities was precisely varied. We varied the standard deviation, skewness, and kurtosis of the distribution of intensities while keeping the mean luminance constant. We found that gain strongly depends on the standard deviation of the distribution. At constant standard deviation, moreover, gain is invariant to changes in skewness or kurtosis. These findings held for both ON and OFF cells, indicating that the measure of contrast is independent of the range of stimulus intensities signaled by the cells. These results confirm the long-held assumption that contrast gain control computes root-mean-square contrast. They also show that contrast gain control senses the full distribution of intensities and leaves unvaried the relative responses of the different cell types. The advantages to visual processing of this remarkably specific computation are not entirely known.

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Section: Discussionmentioning

confidence: 99%

The Statistical Computation Underlying Contrast Gain Control

Bonin

Mante

Carandini

2006

Journal of Neuroscience

107

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“…The frequency power spectrum of stimuli spanned 0 -8000 Hz. The stimulus set consisted of prerecorded (2-4 s) sentences from the phonetically transcribed Texas Instruments/Massachusetts Institute of Technology (TIMIT) speech corpus with 1 s silent intervals between each sentence presentation (Garofolo et al, 1993). Each participant was presented 484 -499 sentences.…”

Section: Stimulimentioning

confidence: 99%

“…After ECoG array placement, participants were asked to passively listen to 15-25 min of natural speech, which consisted of prerecorded (2-4 s) sentences from the phonetically transcribed TIMIT speech corpus (Garofolo et al, 1993). After data collection, an STRF was computed off-line from the local field potential signal at each cortical site to generate an STG STRF map in each subject (Fig.…”

Section: Strf Mapsmentioning

confidence: 99%

“…Data suggest receptive fields and response properties of neurons are matched to the statistics of natural inputs to maximize efficiency of information transmission (Dong and Atick, 1995;Rieke et al, 1995;Dan et al, 1996;Olshausen and Field, 1996;Hsu et al, 2004). In higher-order areas, systems become more selective for natural stimuli and less responsive to synthetic or noise stimuli (Theunissen et al, 2000;Wilkinson et al, 2000;Felsen et al, 2005;Talebi and Baker, 2012). In areas that respond broadly to both synthetic and natural stimuli, natural inputs can push neurons into different operating ranges and activate nonlinearities that evoke response properties not present with synthetic stimuli alone Talebi and Baker, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli

et al. 2016

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The human superior temporal gyrus (STG) is critical for speech perception, yet the organization of spectrotemporal processing of speech within the STG is not well understood. Here, to characterize the spatial organization of spectrotemporal processing of speech across human STG, we use high-density cortical surface field potential recordings while participants listened to natural continuous speech. While synthetic broad-band stimuli did not yield sustained activation of the STG, spectrotemporal receptive fields could be reconstructed from vigorous responses to speech stimuli. We find that the human STG displays a robust anterior-posterior spatial distribution of spectrotemporal tuning in which the posterior STG is tuned for temporally fast varying speech sounds that have relatively constant energy across the frequency axis (low spectral modulation) while the anterior STG is tuned for temporally slow varying speech sounds that have a high degree of spectral variation across the frequency axis (high spectral modulation). This work illustrates organization of spectrotemporal processing in the human STG, and illuminates processing of ethologically relevant speech signals in a region of the brain specialized for speech perception.

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“…Previous studies used relatively simple synthetic stimuli to probe both neural and behavioral performance. However, cortical neurons can display highly nonlinear responses when probed using complex natural stimuli (Theunissen et al, 2000;Bar-Yosef et al, 2002;David et al, 2004;Machens et al, 2004;Felsen et al, 2005;Sharpee et al, 2006). Thus, whether single cortical neurons can match behavioral performance in a task involving complex natural stimuli remains unknown.…”

Section: Introductionmentioning

confidence: 99%

Cortical Discrimination of Complex Natural Stimuli: Can Single Neurons Match Behavior?

Wang¹,

Narayan²,

Graña³

et al. 2007

J. Neurosci.

101

105

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A central finding in many cortical areas is that single neurons can match behavioral performance in the discrimination of sensory stimuli. However, whether this is true for natural behaviors involving complex natural stimuli remains unknown. Here we use the model system of songbirds to address this problem. Specifically, we investigate whether neurons in field L, the homolog of primary auditory cortex, can match behavioral performance in the discrimination of conspecific songs. We use a classification framework based on the (dis)similarity between single spike trains to quantify neural discrimination. We use this framework to investigate the discriminability of single spike trains in field L in response to conspecific songs, testing different candidate neural codes underlying discrimination. We find that performance based on spike timing is significantly higher than performance based on spike rate and interspike intervals. We then assess the impact of temporal correlations in spike trains on discrimination. In contrast to widely discussed effects of correlations in limiting the accuracy of a population code, temporal correlations appear to improve the performance of single neurons in the majority of cases. Finally, we compare neural performance with behavioral performance. We find a diverse range of performance levels in field L, with neural performance matching behavioral accuracy only for the best neurons using a spike-timing-based code.

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Cortical Sensitivity to Visual Features in Natural Scenes

Cited by 147 publications

References 54 publications

The Statistical Computation Underlying Contrast Gain Control

The Statistical Computation Underlying Contrast Gain Control

Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli

Cortical Discrimination of Complex Natural Stimuli: Can Single Neurons Match Behavior?

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