Synchronized Firing among Retinal Ganglion Cells Signals Motion Reversal

Schwartz, Greg; Sh, Taylor; Fisher, Clark; Harris, Rob; Berry, Michael

doi:10.1016/j.neuron.2007.07.042

Cited by 104 publications

(125 citation statements)

References 46 publications

(59 reference statements)

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“…The internal functional architecture of our models match that of the retina at the level of individual neurons, and moreover our models generalize from natural scenes, but not white noise, to a wide range of artificially structured stimuli with vastly different statistics. Thus this work provides quantitative validation for the deep learning approach to neuroscience in an experimentally accessible sensory circuit, places decades of work [7][8][9][10][11][12][13][14][15] on retinal responses to artificially structured stimuli on much firmer foundations of ethological relevance, and highlights the fundamental importance of studying sensory circuit responses to natural stimuli.…”

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confidence: 79%

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The dynamic neural code of the retina for natural scenes

Maheswaranathan

Tanaka

et al. 2018

Preprint

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The normal function of the retina is to convey information about natural visual images. It is this visual environment that has driven evolution, and that is clinically relevant. Yet nearly all of our understanding of the neural computations, biological function, and circuit mechanisms of the retina comes in the context of artificially structured stimuli such as flashing spots, moving bars and white noise. It is fundamentally unclear how these artificial stimuli are related to circuit processes engaged under natural stimuli. A key barrier is the lack of methods for analyzing retinal responses to natural images. We addressed both these issues by applying convolutional neural network models (CNNs) to capture retinal responses to natural scenes. We find that CNN models predict natural scene responses with high accuracy, achieving performance close to the fundamental limits of predictability set by intrinsic cellular variability. Furthermore, individual internal units of the model are highly correlated with actual retinal interneuron responses that were recorded separately and never presented to the model during training. Finally, we find that models fit only to natural scenes, but not white noise, reproduce a range of phenomena previously described using distinct artificial stimuli, including frequency doubling, latency encoding, motion anticipation, fast contrast adaptation, synchronized responses to motion reversal and object motion sensitivity. Further examination of the model revealed extremely rapid context dependence of retinal feature sensitivity under natural scenes using an analysis not feasible from direct examination of retinal responses. Overall, these results show that nonlinear retinal processes engaged by artificial stimuli are also engaged in and relevant to natural visual processing, and that CNN models form a powerful and unifying tool to study how sensory circuitry produces computations in a natural context.

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confidence: 79%

“…3A), latency encoding 10 (Fig. 3B), synchronized responses to motion reversal 11 (Fig. 3C), motion anticipation 12 (Fig.…”

Section: Cnns Replicate Wide Range Of Retinal Phenomenamentioning

confidence: 98%

The dynamic neural code of the retina for natural scenes

Maheswaranathan

Tanaka

et al. 2018

Preprint

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“…Second, correlated activities of retinal neurons drive their common post-synaptic neurons in lateral geniculate nucleus (LGN) more effectively and efficiently because of spatial summation (Usrey et al, 1998;. Third, it is also reported that correlated activities are related to some specific functions of retina, such as color discrimination (Chen et al, 2004) and motion detection (Schwartz et al, 2007).…”

Section: The Significance Of Correlated Firings Among Neuronsmentioning

confidence: 99%

Spikes with short inter-spike intervals in frog retinal ganglion cells are more correlated with their adjacent neurons’ activities

et al. 2011

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Correlated firings among neurons have been extensively investigated; however, previous studies on retinal ganglion cell (RGC) population activities were mainly based on analyzing the correlated activities between the entire spike trains. In the present study, the correlation properties were explored based on burst-like activities and solitary spikes separately. The results indicate that: (1) burst-like activities were more correlated with other neurons' activities; (2) burst-like spikes correlated with their neighboring neurons represented a smaller receptive field than that of correlated solitary spikes. These results suggest that correlated burst-like spikes should be more efficient in signal transmission, and could encode more detailed spatial information.

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“…One possibility is that object motion is detected as early the retina, where a subset of retinal ganglion cells (RGCs) respond to differential motion of their receptive field center and surround (Olveczky et al, 2003;Zhang et al, 2012). However, these cells, like RGCs that respond to motion onset or reversal (Schwartz et al, 2007;Chen et al, 2013) or RGCs that are tuned to movement speed and direction (Oyster, 1968), also respond to the sudden appearance/disappearance of stationary stimuli and therefore do not specifically signal object motion.…”

Section: Introductionmentioning

confidence: 99%

Active Dendritic Properties and Local Inhibitory Input Enable Selectivity for Object Motion in Mouse Superior Colliculus Neurons

Gale

Murphy

2016

Journal of Neuroscience

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Neurons respond to specific features of sensory stimuli. In the visual system, for example, some neurons respond to motion of small but not large objects, whereas other neurons prefer motion of the entire visual field. Separate neurons respond equally to local and global motion but selectively to additional features of visual stimuli. How and where does response selectivity emerge? Here, we show that wide-field (WF) cells in retino-recipient layers of the mouse superior colliculus (SC) respond selectively to small moving objects. Moreover, we identify two mechanisms that contribute to this selectivity. First, we show that input restricted to a small portion of the broad dendritic arbor of WF cells is sufficient to trigger dendritic spikes that reliably propagate to the soma/axon. In vivo whole-cell recordings reveal that nearly every action potential evoked by visual stimuli has characteristics of spikes initiated in dendrites. Second, inhibitory input from a different class of SC neuron, horizontal cells, constrains the range of stimuli to which WF cells respond. Horizontal cells respond preferentially to the sudden appearance or rapid movement of large stimuli. Optogenetic reduction of their activity reduces movement selectivity and broadens size tuning in WF cells by increasing the relative strength of responses to stimuli that appear suddenly or cover a large region of space. Therefore, strongly propagating dendritic spikes enable small stimuli to drive spike output in WF cells and local inhibition helps restrict responses to stimuli that are both small and moving.

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Synchronized Firing among Retinal Ganglion Cells Signals Motion Reversal

Cited by 104 publications

References 46 publications

The dynamic neural code of the retina for natural scenes

The dynamic neural code of the retina for natural scenes

Spikes with short inter-spike intervals in frog retinal ganglion cells are more correlated with their adjacent neurons’ activities

Active Dendritic Properties and Local Inhibitory Input Enable Selectivity for Object Motion in Mouse Superior Colliculus Neurons

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