Rapid Neural Coding in the Retina with Relative Spike Latencies

Gollisch, Tim; Meister, Markus

doi:10.1126/science.1149639

Cited by 572 publications

(526 citation statements)

References 29 publications

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“…Remarkably, the CNN model exhibited fast contrast 7-9 adaptation (Fig. 3A), latency encoding 10 (Fig. 3B), synchronized responses to motion reversal 11 (Fig.…”

Section: Cnns Replicate Wide Range Of Retinal Phenomenamentioning

confidence: 99%

“…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: 99%

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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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“…Remarkably, the CNN model exhibited fast contrast 7-9 adaptation (Fig. 3A), latency encoding 10 (Fig. 3B), synchronized responses to motion reversal 11 (Fig.…”

Section: Cnns Replicate Wide Range Of Retinal Phenomenamentioning

confidence: 99%

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confidence: 99%

The dynamic neural code of the retina for natural scenes

Maheswaranathan

Tanaka

et al. 2018

Preprint

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“…Given previous findings that neurons carry substantial sensory information in their response latencies (Panzeri et al, 2001;Reich et al, 2001;Gollisch and Meister, 2008), consideration of temporal correlations across the time bins may be important. Statistical models that take account of time-lagged correlations can be constructed based on the maximum entropy method with a Markovian assumption of temporal evolution (Marre et al, 2009) or based on a generalized linear model (Pillow et al, 2005(Pillow et al, , 2008.…”

Section: Temporal Correlations Across Time Binsmentioning

confidence: 99%

Mismatched Decoding in the Brain

Oizumi

Ishii²,

Ishibashi³

et al. 2010

J. Neurosci.

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"How is information decoded in the brain?" is one of the most difficult and important questions in neuroscience. We have developed a general framework for investigating to what extent the decoding process in the brain can be simplified. First, we hierarchically constructed simplified probabilistic models of neural responses that ignore more than Kth-order correlations using the maximum entropy principle. We then computed how much information is lost when information is decoded using these simplified probabilistic models (i.e., "mismatched decoders"). To evaluate the information obtained by mismatched decoders, we introduced an information theoretic quantity, I*, which was derived by extending the mutual information in terms of communication rate across a channel. We showed that I* provides consistent results with the minimum mean-square error as well as the mutual information, and demonstrated that a previously proposed measure quantifying the importance of correlations in decoding substantially deviates from I* when many cells are analyzed. We then applied this proposed framework to spike data for vertebrate retina using short natural scene movies of 100 ms duration as a set of stimuli and computing the information contained in neural activities. Although significant correlations were observed in population activities of ganglion cells, information loss was negligibly small even if all orders of correlation were ignored in decoding. We also found that, if we inappropriately assumed stationarity for long durations in the information analysis of dynamically changing stimuli, such as natural scene movies, correlations appear to carry a large proportion of total information regardless of their actual importance.

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“…However, in most neuronal systems, neural activities are in the form of time series of spikes. Furthermore, stimulus representation in some sensory systems are characterized by a small number of precisely timed spikes [3,4], suggesting that the brain possesses a machinery for extracting information embedded in the timings of spikes, not only in their overall rate. Thus, understanding the power and limitations of spike-timing based computation and learning is of fundamental importance in computational neuroscience.…”

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confidence: 99%

Theory of Spike Timing-Based Neural Classifiers

2010

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We study the computational capacity of a model neuron, the Tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast to its static analog, the Perceptron, the Tempotron's solutions space consists of a large number of small clusters of weight vectors. The capacity of the system per synapse is finite in the large size limit and weakly diverges with the stimulus duration relative to the membrane and synaptic time constants.PACS numbers: 87.18. Sn, 87.19.ll, 86.19.lv Neural network models of supervised learning are usually concerned with processing static spatial patterns of intensities. A famous example is learning in a singlelayer binary neuron, the Perceptron [1, 2]. However, in most neuronal systems, neural activities are in the form of time series of spikes. Furthermore, stimulus representation in some sensory systems are characterized by a small number of precisely timed spikes [3,4], suggesting that the brain possesses a machinery for extracting information embedded in the timings of spikes, not only in their overall rate. Thus, understanding the power and limitations of spike-timing based computation and learning is of fundamental importance in computational neuroscience. Gütig and Sompolinsky [5] have recently suggested a simple model, the Tempotron, for decoding information embedded in spatio-temporal spike patterns. The Tempotron is an Integrate and Fire (IF) neuron, with N input synapses of strength ω i , i = 1, . . . , N . Each input pattern is represented by N sequences of spikes, where the spike timings for the afferent i are denoted by {t i }. The membrane potential is given bywhere u(t) denotes a fixed causal temporal kernel. An example is the difference of exponentials form:, where τ m and τ s correspond, respectively, to the membrane and synaptic time constants [6]. The Tempotron fires a spike whenever U crosses the threshold, U th , from below [7] (Fig. 1a). The Tempotron performs a binary classification of its input patterns by firing one or more output spikes when presented with a 'target' (+1) pattern and remaining quiescent during a 'null' (-1) pattern.In this Letter we present a theoretical study of the computational power of the Tempotron. We focus on the standard task of classifying a batch of P = αN random patterns, where α denotes the number of patterns per input synapse. For each pattern, the timings of the input spikes from each input neuron are randomly chosen from independent Poisson processes with rate 1 T , where T is the duration of the input patterns, and the desired output, y = ±1, is randomly and independently chosen with equal probabilities. A solution to the classification problem is a set of synaptic weights {ω i } that yields a correct classification of all P patterns. We will address several fundamental questions. First, numerical simulations based on a simple error-correcting on-line learning algorithm suggest that...

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Rapid Neural Coding in the Retina with Relative Spike Latencies

Cited by 572 publications

References 29 publications

The dynamic neural code of the retina for natural scenes

The dynamic neural code of the retina for natural scenes

Mismatched Decoding in the Brain

Theory of Spike Timing-Based Neural Classifiers

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