A Retinal Circuit That Computes Object Motion

Baccus, Stephen A.; Ölveczky, Bence P.; Manu, Mihai; Meister, Markus

doi:10.1523/jneurosci.4206-07.2008

Cited by 177 publications

(212 citation statements)

References 32 publications

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“…Studies in the visual system have implicated early structures like the primary visual cortex and even the retina in some species in the processing of local vs. global stimulus properties to account fro scene segmentation (e.g., Olveczky et al 2003;Baccus et al 2008;Nothdurft 1994). Very few studies of the auditory system have looked at neuronal responses in relation to scene segmentation, and they have been mainly in the auditory cortex (e.g.…”

Section: Bohlen Et Al: Detection Of Modulated Tones In Modulated Noisementioning

confidence: 99%

Detection of Modulated Tones in Modulated Noise by Non-human Primates

Bohlen

Dylla

Timms

et al. 2014

JARO

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In natural environments, many sounds are amplitudemodulated. Amplitude modulation is thought to be a signal that aids auditory object formation. A previous study of the detection of signals in noise found that when tones or noise were amplitude-modulated, the noise was a less effective masker, and detection thresholds for tones in noise were lowered. These results suggest that the detection of modulated signals in modulated noise would be enhanced. This paper describes the results of experiments investigating how detection is modified when both signal and noise were amplitude-modulated. Two monkeys (Macaca mulatta) were trained to detect amplitude-modulated tones in continuous, amplitude-modulated broadband noise. When the phase difference of otherwise similarly amplitude-modulated tones and noise were varied, detection thresholds were highest when the modulations were in phase and lowest when the modulations were anti-phase. When the depth of the modulation of tones or noise was varied, detection thresholds decreased if the modulations were antiphase. When the modulations were in phase, increasing the depth of tone modulation caused an increase in tone detection thresholds, but increasing depth of noise modulations did not affect tone detection thresholds. Changing the modulation frequency of tone or noise caused changes in threshold that saturated at modulation frequencies higher than 20 Hz; thresholds decreased when the tone and noise modulations were in phase and decreased when they were anti-phase. The relationship between reaction times and tone level were not modified by manipulations to the nature of temporal variations in the signal or noise. The changes in behavioral threshold were consistent with a model where the brain subtracted noise from signal. These results suggest that the parameters of the modulation of signals and maskers heavily influence detection in very predictable ways. These results are consistent with some results in humans and avians and form the baseline for neurophysiological studies of mechanisms of detection in noise.

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Section: Bohlen Et Al: Detection Of Modulated Tones In Modulated Noisementioning

confidence: 99%

Detection of Modulated Tones in Modulated Noise by Non-human Primates

Bohlen

Dylla

Timms

et al. 2014

JARO

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“…Inhibitory transmission to ganglion cells is known to have different effects, including reducing activity (6,10), or increasing activity through disinhibition (11)(12)(13)(14). Thus, to understand how a particular amacrine cell changes the ganglion cell response, measurements of both the response and transmission of the interneuron must be temporally precise.…”

mentioning

confidence: 99%

Disinhibitory gating of retinal output by transmission from an amacrine cell

Manu

Baccus²

2011

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

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Inhibitory interneurons help transform the input of a neural circuit into its output. Such interneurons are diverse, and most have unknown function. To study the function of single amacrine cells in the intact salamander retina, we recorded extracellularly from a population of ganglion cells with a multielectrode array, while simultaneously recording from or injecting current into single Offtype amacrine cells that had linear responses. We measured how visual responses of the amacrine cell interacted both with other visual input to the ganglion cell and with transmission between the two cells. We found that on average, visual responses from Off-type amacrine cells inhibited nearby Off-type ganglion cells. By recording and playing back the light-driven membrane potential fluctuations of amacrine cells during white noise visual stimuli, we found that paradoxically, increasing the light-driven modulations of inhibitory amacrine cells increased the firing rate of nearby Off-type ganglion cells. By measuring the correlations and transmission between amacrine and ganglion cells, we found that, on average, the amacrine cell hyperpolarizes before the ganglion cell fires, generating timed disinhibition just before the ganglion cell spikes. In addition, we found that amacrine to ganglion cell transmission is nonlinear in that increases in ganglion cell activity produced by amacrine hyperpolarization were greater than decreases in activity produced by amacrine depolarization. We conclude that the primary mode of action of this class of amacrine cell is to actively gate the ganglion cell response by a timed release from inhibition.multielectrode recording | computational modeling I n local neural circuits of the brain, interneurons change the output of the circuit by combining their own transmission with inputs to the circuit (1). Inhibitory interneurons throughout the brain have great diversity in their morphology, postsynaptic connections, and biochemistry, but for nearly all of these cells, their functional role in information processing is unknown. In the retina, inhibitory amacrine cells comprise more than 30 classes; most of these cells also have unknown function (2, 3).Compared with a principal neuron, which represents a circuit's output, understanding the functional role of interneurons is a challenge because the effect of an interneuron on the circuit's output is a combination of multiple circuit properties (4, 5). For example, the effect of an amacrine cell on a retinal ganglion cell is a function of the amacrine cell's light response, its effects of transmission on the retinal ganglion cell, and of other visual input to the ganglion cell. Knowledge of all three of these properties is needed to predict how the amacrine cell will affect the ganglion cell's activity.The responses of different cells are precisely timed in the retina, with different amacrine and ganglion cells having distinct temporal responses with respect to light (6-9). Inhibitory transmission to ganglion cells is known to have different effects, i...

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“…7 illustrates, blocking HC-to-HC gap junctions slightly enhances the GC response out to radii of several hundreds of lm. This indicates the existence of lateral inhibition at HC level but this inhibition is not wide and strong enough so that HC network is not involved in the object segregation mechanism (Baccus et al 2008). On the other hand, blocking wf-AC completely eliminates the wide-field inhibition that extends more than 2,000 lm.…”

Section: Discussionmentioning

confidence: 93%

“…In case of indirect suppression, wf-AC would be excited by separate BC terminals as GC would receive presynaptically suppressed signal from BC terminals; because we know that wf-AC itself is not suppressed by global motion (Olveczky et al 2003;Baccus et al 2008;Roska and Werblin 2003). In such a case, the model BC should have been modified to have separate excitatory and inhibitory terminals that should concurrently send signals to wf-AC and GC, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…It has been known that the ''wide-field'' amacrine cells extend their multiple axonal processes toward the distant regions across the retinal horizontal plane (e.g., Dacey 1989;Famiglietti 1992a, b;Lin and Masland 2006), and can inhibit ganglion cells directly (Cook and McReynolds 1998) and/or indirectly (Shields and Lukasiewicz 2003). More recently, a study showed that such wide-field amacrine cells selectively suppress the excitatory synaptic inputs onto the ganglion cells which are sensitive to the object motion (Baccus et al 2008). In that study, also an abstract model of the retinal circuit was proposed by employing: (1) a ''linear-nonlinear'' (LN) cascade model (i.e., the cascade with a linear spatio-temporal filter followed by a static nonlinearity) for the input-output dynamics of the outer retinal circuit [in turn, this determines the excitatory inputs onto ganglion/ amacrine cells]; and (2) arithmetic division for the inhibitory input from the amacrine cell in the background region onto the ganglion cell in the object region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A retinal circuit model accounting for wide-field amacrine cells

2008

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In previous experimental studies on the visual processing in vertebrates, higher-order visual functions such as the object segregation from background were found even in the retinal stage. Previously, the ''linear-nonlinear'' (LN) cascade models have been applied to the retinal circuit, and succeeded to describe the input-output dynamics for certain parts of the circuit, e.g., the receptive field of the outer retinal neurons. And recently, some abstract models composed of LN cascades as the circuit elements could explain the higher-order retinal functions. However, in such a model, each class of retinal neurons is mostly omitted and thus, how those neurons play roles in the visual computations cannot be explored. Here, we present a spatio-temporal computational model of the vertebrate retina, based on the response function for each class of retinal neurons and on the anatomical inter-cellular connections. This model was capable of not only reproducing the spatio-temporal filtering properties of the outer retinal neurons, but also realizing the object segregation mechanism in the inner retinal circuit involving the ''widefield'' amacrine cells. Moreover, the first-order Wiener kernels calculated for the neurons in our model showed a reasonable fit to the kernels previously measured in the real retinal neuron in situ.

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A Retinal Circuit That Computes Object Motion

Cited by 177 publications

References 32 publications

Detection of Modulated Tones in Modulated Noise by Non-human Primates

Detection of Modulated Tones in Modulated Noise by Non-human Primates

Disinhibitory gating of retinal output by transmission from an amacrine cell

A retinal circuit model accounting for wide-field amacrine cells

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