Fidelity of the Ensemble Code for Visual Motion in Primate Retina

Fréchette, Éric; Sher, Alexander; Grivich, Matthew I.; Petrusca, Dumitru; Litke, A. M.; Chichilnisky, E. J.

doi:10.1152/jn.01175.2004

Cited by 128 publications

(183 citation statements)

References 64 publications

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“…Analyses of retinal responses to moving stimuli show that the direction of fast-moving stimuli can be encoded at a very fine time scale (Chichilnisky and Kalmar, 2003;Borghuis, 2003;Frechette et al, 2005). Optimal encoding of a ϳ20°/s stimulus requires integration (i.e., low-pass filtering) of ganglion cell responses for ϳ10 ms, but sufficient information about motion direction is obtained even if the retinal responses are integrated for just over 1 ms.…”

Section: Discussionmentioning

confidence: 99%

Fine Temporal Properties of Center–Surround Interactions in Motion Revealed by Reverse Correlation

2006

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Center-surround interactions are a key property of visual motion mechanisms. Using a temporal reverse correlation method with human observers, we investigated perceptual interactions between a brief center motion (ϳ20 ms) and a surround that moved up-down with a new direction chosen randomly every 5 ms. The aim was to reveal interactions between center and surround motions and their dependency on relative direction, contrast, and timing. Hypothesizing that surround computation involves different neural circuitry than the center response, we manipulated surround contrast to affect the relative timing of center and surround signals. The reverse correlation analysis yielded temporal profiles of surround influence indicating, in 5 ms steps, the time course of the effect of the surround on the discriminability of center motion. The resulting temporal profiles varied systematically with contrast: as surround contrast decreased, both the latency and duration of its influence increased. This finding, consistent with longer and variable neural response latencies at low contrast, psychophysically reveals fine-scale temporal interactions between center and surround signals. Additionally, the strength of surround influence was correlated with psychophysical thresholds for discriminating center motion. The directionality of this relationship, however, depended only on center contrast. When center motion was high contrast, poor direction discrimination was associated with an increased probability of same-direction surround motions. Low-contrast center motion, however, was more discriminable when surrounded by motion in the same direction, regardless of surround contrast. This suggests that the previously reported adaptive nature of center-surround interactions in motion is driven primarily by the visibility of the center motion signals.

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Section: Discussionmentioning

confidence: 99%

Fine Temporal Properties of Center–Surround Interactions in Motion Revealed by Reverse Correlation

2006

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“…1 B) was sampled at 20 kHz and sorted automatically as described previously (Litke et al, 2004;Frechette et al, 2005;Shlens et al, 2007). Briefly, signals that crossed a threshold of 3 SDs were marked, and the waveforms found on the marked electrode and its six adjacent neighbors were projected into five dimensional principal component space.…”

Section: Methodsmentioning

confidence: 99%

A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro

et al. 2008

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Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90 -99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 Ϯ 7% (mean Ϯ SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

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“…The analog signal from each electrode was sampled at 20 kHz. This system has been used previously to study the encoding of visual motion and multineuron firing patterns in mosaics of parasol cells in the primate retina (Frechette et al, 2005;Shlens et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

Identification and Characterization of a Y-Like Primate Retinal Ganglion Cell Type

Petrusca

Grivich²,

Sher³

et al. 2007

J. Neurosci.

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154

227

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The primate retina communicates visual information to the brain via a set of parallel pathways that originate from at least 22 anatomically distinct types of retinal ganglion cells. Knowledge of the physiological properties of these ganglion cell types is of critical importance for understanding the functioning of the primate visual system. Nonetheless, the physiological properties of only a handful of retinal ganglion cell types have been studied in detail. Here we show, using a newly developed multielectrode array system for the large-scale recording of neural activity, the existence of a physiologically distinct population of ganglion cells in the primate retina with distinctive visual response properties. These cells, which we will refer to as upsilon cells, are characterized by large receptive fields, rapid and transient responses to light, and significant nonlinearities in their spatial summation. Based on the measured properties of these cells, we speculate that they correspond to the smooth/large radiate cells recently identified morphologically in the primate retina and may therefore provide visual input to both the lateral geniculate nucleus and the superior colliculus. We further speculate that the upsilon cells may be the primate retina's counterparts of the Y-cells observed in the cat and other mammalian species.

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Fidelity of the Ensemble Code for Visual Motion in Primate Retina

Cited by 128 publications

References 64 publications

Fine Temporal Properties of Center–Surround Interactions in Motion Revealed by Reverse Correlation

Fine Temporal Properties of Center–Surround Interactions in Motion Revealed by Reverse Correlation

A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical NetworksIn Vitro

Identification and Characterization of a Y-Like Primate Retinal Ganglion Cell Type

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