Dynamical Adaptation in Photoreceptors

Clark, Damon A.; Benichou, Raphael; Meister, Markus; Silveira, Rava Azeredo da

doi:10.1371/journal.pcbi.1003289

Cited by 59 publications

(92 citation statements)

References 87 publications

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“…However, classic linear-nonlinear models with a single linear component fail to capture such effects. This motivates the need for a biophysically plausible modeling framework that can explain RGC responses across stimulus conditions (Ozuysal & Baccus, 2012a;Clark et al, 2013;Cui et al, 2016a).…”

Section: Capturing Spike Responses Across Contrastsmentioning

confidence: 99%

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

Latimer

2018

Preprint

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A popular approach to the study of information processing in the nervous system is to characterize neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe the neuron's stimulus integration properties, followed by a rectifying nonlinearity to convert filter output to spike rate. However, real neurons integrate stimuli via the modulation of nonlinear excitatory and inhibitory synaptic conductances. Here we introduce a biophysically inspired point process model with conductance-based inputs. The model provides a novel interpretation of the popular Poisson generalized linear model (GLM) as a special kind of conductance-based model, where excitatory and inhibitory conductances are modulated in a "push-pull" manner so that total conductance remains constant. We relax this constraint to obtain a more general and flexible "conductance-based encoding model" (CBEM), which can exhibit stimulus-dependent fluctuations in gain and dynamics. We fit the model to spike trains of macaque retinal ganglion cells and show that, remarkably, we can accurately infer underlying inhibitory and excitatory conductances, using comparisons to intracellularly measured conductances. Using extracellular data, we corroborate the intracellular finding that synaptic excitation temporally precedes inhibition in retina. We show that the CBEM outperforms the classic GLM at predicting retinal ganglion cell responses to full-field stimuli, generalizes better across contrast levels, and captures inhibition-dependent response properties to spatially structured stimuli. The CBEM provides a powerful tool for gaining insights into the intracellular variables governing spiking, and forges an important link between extracellular characterization methods and biophysically detailed response models.

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Section: Capturing Spike Responses Across Contrastsmentioning

confidence: 99%

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

Latimer

2018

Preprint

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“…Receptive fields therefore represent a linearized form of the full nonlinear neural response under specific conditions and likely include components of (nonlinear) adaptation. For example, in models that explicitly include nonlinear gain adaptation, the linear receptive field incorporates components both from direct excitation and from the gain adaptation [45]. …”

Section: Input Circuits Are Tuned For Specific Spatial and Temporal Smentioning

confidence: 99%

“…Hence, strong lateral inhibition and fast temporal integration are observed under brighter conditions with relatively high signal-to-noise [34,47,58–61]. Accordingly, and in spite of the different phototransduction cascades, both insect and vertebrate photoreceptors respond to light more quickly at higher luminance levels (Figure 3C; reviewed in [45]) [27,34,62–65]. …”

Section: Input Circuits Are Tuned For Specific Spatial and Temporal Smentioning

confidence: 99%

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Parallel Computations in Insect and Mammalian Visual Motion Processing

Clark

Demb

2016

Current Biology

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Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.

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“…Luminance adaptation (adapting to the mean light intensity) is mediated by photoreceptor cells, and could be modeled by prepending a simple photoreceptor model (e.g. [69]) to an LN-LN model. There are two major sites of contrast adaptation, at the bipolar-to-ganglion cell synapse [70,71] and at the spiking mechanism of ganglion cells [70,72].…”

Section: Discussionmentioning

confidence: 99%

Inferring hidden structure in multilayered neural circuits

Maheswaranathan

Baccus

Ganguli

2017

Preprint

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A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. 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. 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. For example, with our regularization framework, we can learn the STA and STC using 5 and 10 minutes of data, respectively, at a level of accuracy that otherwise requires 40 minutes of data without regularization. We apply our framework to retinal ganglion cell processing, learning cascaded linear-nonlinear (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 had consistently high thresholds, 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 general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data. Author SummaryComputation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuit's output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapp...

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Dynamical Adaptation in Photoreceptors

Cited by 59 publications

References 87 publications

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

Parallel Computations in Insect and Mammalian Visual Motion Processing

Inferring hidden structure in multilayered neural circuits

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