A simple white noise analysis of neuronal light responses

Chichilnisky, E. J.

doi:10.1080/713663221

Cited by 685 publications

(562 citation statements)

References 0 publications

Supporting

Mentioning

537

Contrasting

Order By: Relevance

“…The spot intensity was modulated in a pseudo-random fashion, and the relation between this dynamic flicker stimulus and the neuron's firing rate was fit with a simple linear–nonlinear (LN) model (Figure 1B; see Materials and Methods). The model yields two functions that characterize the neuron's response: the “filter” specifies how light intensity is integrated over time, and the “nonlinearity” accounts for distortions of the response, such as rectification at the bottom and saturation at the top of the firing range [21]. The goal was to test how these basic parameters of the light response were affected by global image shifts.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On

2007

View full text Add to dashboard Cite

Retinal ganglion cells are commonly classified as On-center or Off-center depending on whether they are excited predominantly by brightening or dimming within the receptive field. Here we report that many ganglion cells in the salamander retina can switch from one response type to the other, depending on stimulus events far from the receptive field. Specifically, a shift of the peripheral image—as produced by a rapid eye movement—causes a brief transition in visual sensitivity from Off-type to On-type for approximately 100 ms. We show that these ganglion cells receive inputs from both On and Off bipolar cells, and the Off inputs are normally dominant. The peripheral shift strongly modulates the strength of these two inputs in opposite directions, facilitating the On pathway and suppressing the Off pathway. Furthermore, we identify certain wide-field amacrine cells that contribute to this modulation. Depolarizing such an amacrine cell affects nearby ganglion cells in the same way as the peripheral image shift, facilitating the On inputs and suppressing the Off inputs. This study illustrates how inhibitory interneurons can rapidly gate the flow of information within a circuit, dramatically altering the behavior of the principal neurons in the course of a computation.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The relation between ganglion cell firing rate and visual stimulus was fitted by an LN model [21,23] or by a 2LN model. The intensity s ( t ) of the circular spot was measured from the video monitor at 1-ms resolution, and normalized to have zero mean, a standard deviation equal to the contrast (0.3), and dimensionless units.…”

Section: Methodsmentioning

confidence: 99%

Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On

2007

View full text Add to dashboard Cite

show abstract

“…The sensitivity of the neuron to each of its significant eigenvectors in a stimulus ensemble is measured by plotting the average neuronal response as a function of feature contrast (Figure 1D), yielding the contrast-response function [26,28,29] (see Materials and Methods). A steep contrast-response function indicates a high sensitivity of the neuron to the presence of the corresponding feature in the images, whereas a flat contrast-response function indicates that the neuronal response is insensitive to the presence of the feature.…”

Section: Resultsmentioning

confidence: 99%

“…For each feature, the contrast-response function was measured from the neuronal responses to 1–3 repeats of an ensemble; for nearly all cells (38/40), these repeats were distinct from those used to estimate the preferred features, in order to avoid bias [29]. …”

Section: Methodsmentioning

confidence: 99%

Cortical Sensitivity to Visual Features in Natural Scenes

et al. 2005

View full text Add to dashboard Cite

A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to natural images. We then measured the neuronal sensitivity to these features when they were present in either natural images or random stimuli. We found that in the responses of complex cells, but not of simple cells, the sensitivity was markedly higher for natural images than for random stimuli. Such elevated sensitivity leads to increased detectability of the visual features and thus an improved cortical representation of natural scenes. Interestingly, this effect is due not to the spatial power spectra of natural images, but to their phase regularities. These results point to a distinct visual-coding strategy that is mediated by contextual modulation of cortical responses tuned to the spatial-phase structure of natural scenes.

show abstract

“…These models often begin with a statistical description of the stimuli that precede a neural response such as the spike-triggered average (STA) [1, 2] or covariance (STC) [3–8]. These statistical measures characterize to some extent the set of effective stimuli that drive a response, but do not necessarily reveal how these statistical properties relate to cellular mechanisms or neural pathways.…”

Section: Introductionmentioning

confidence: 99%

Inferring hidden structure in multilayered neural circuits

et al. 2018

View full text Add to dashboard Cite

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.

show abstract

A simple white noise analysis of neuronal light responses

Cited by 685 publications

References 0 publications

Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On

Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On

Cortical Sensitivity to Visual Features in Natural Scenes

Inferring hidden structure in multilayered neural circuits

Contact Info

Product

Resources

About