Biology subjects

Rieke, F.

Publications and source records attributed to Rieke, F..

35 records · Page 2Linked to original sources

bioRxiv2021

Conserved Circuits for Direction Selectivity in the Primate Retina

The detection of motion direction is a fundamental visual function and a classic model for neural computation1,2. In the non-primate mammalian retina, direction selectivity arises in starburst amacrine cell (SAC) dendrites, which provide selective inhibition to ON and ON-OFF direction selective retinal ganglion cells (dsRGCs)3,4. While SACs are present in primates5, their connectivity is unknown and the existence of primate dsRGCs remains an open question. Here we present a connectomic reconstruction of the primate ON SAC circuit from a serial electron microscopy volume of macaque central retina. We show that the structural basis for the SACs ability to compute and confer directional selectivity on post-synaptic RGCs6 is conserved in primates and that SACs selectively target a single ganglion cell type, a candidate homolog to the mammalian ON-sustained dsRGCs that project to the accessory optic system and contribute to gaze-stabilizing reflexes7,8. These results indicate that the capacity to compute motion direction is present in the retina, far earlier in the primate visual system than classically thought, and they shed light on the distinguishing features of primate motion processing by revealing the extent to which ancestral motion circuits are conserved.

neuroscience↗

bioRxiv2021

Dendro-somatic synaptic inputs to ganglion cells violate receptive field and connectivity rules in the mammalian retina

In retinal neurons, morphology strongly influences visual response features. Ganglion cell (GC) dendrites ramify in distinct strata of the inner plexiform layer (IPL) so that GCs responding to light increments (ON) or decrements (OFF) receive appropriate excitatory inputs. This vertical stratification prescribes response polarity and ensures consistent connectivity between cell types, whereas the lateral extent of GC dendritic arbors typically dictates receptive field (RF) size. Here, we identify circuitry in mouse retina that contradicts these conventions. A2 amacrine cells are interneurons understood to mediate "cross-over" inhibition by relaying excitatory input from the ON layer to inhibitory outputs in the OFF layer. Ultrastructural and physiological analyses show, however, that some A2s deliver powerful inhibition to OFF GC somas and proximal dendrites in the ON layer, rendering their inhibitory RFs smaller than their dendritic arbors. This OFF pathway, avoiding entirely the OFF region of the IPL, challenges several tenets of retinal circuitry.

neuroscience↗

bioRxiv2021

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of On parasol retinal ganglion cells to spatial structure in natural images

Neural circuits are constructed from nonlinear building blocks, and not surprisingly overall circuit behavior is often strongly nonlinear. But neural circuits can also behave near linearly, and some circuits shift from linear to nonlinear behavior depending on stimulus conditions. Such control of the linearity or nonlinearity of circuit behavior is fundamental to neural computation. Here we study a surprising stimulus dependence of the responses of On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in temporally-modulated grating stimuli but linearly to spatial structure in flashed gratings and natural visual inputs. We show that this unexpected response linearity can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least in part from adaptation in the cone photoreceptors. More generally, this highlights how subtle asymmetries in signaling - here in the cone signals - can qualitatively alter circuit computation.

neuroscience↗

bioRxiv2021

Multiple time scales of adaptation allow cones to encode the inputs created by visual exploration of natural scenes

Primates explore their visual environment by making frequent saccades, discrete and ballistic eye movements that direct the fovea to specific regions of interest. Saccades produce large and rapid changes in input. The magnitude of these changes and the limited signaling range of visual neurons means that effective encoding requires rapid adaptation. Here, we explore how cone photoreceptors maintain sensitivity under these conditions. Adaptation makes cone responses to naturalistic stimuli highly nonlinear and dependent on stimulus history. Such responses cannot be explained by linear or linear-nonlinear models but are well explained by a biophysical model of phototransduction with fast and slow adaptational mechanisms. The resulting model can predict cone responses to a broad range of stimuli and enables the design of stimuli that elicit specific (e.g. linear) cone photocurrents. These advances will provide a foundation for investigating the contributions of cones and post-cone processing to visual function.

neuroscience↗

bioRxiv2020

Predictive encoding of motion begins in the primate retina

Survival in the natural environment often relies on an animals ability to quickly and accurately predict the trajectories of moving objects. Motion prediction is primarily understood in the context of translational motion, but the environment contains other types of behaviorally salient motion, such as that produced by approaching or receding objects. However, the neural mechanisms that detect and predictively encode these motion types remain unclear. Here, we address these questions in the macaque monkey retina. We report that four of the parallel output pathways in the primate retina encode predictive information about the future trajectory of moving objects. Predictive encoding occurs both for translational motion and for higher-order motion patterns found in natural vision. Further, predictive encoding of these motion types is nearly optimal with transmitted information approaching the theoretical limit imposed by the stimulus itself. These findings argue that natural selection has emphasized encoding of information that is relevant for anticipating future properties of the environment.

neuroscience↗

bioRxiv2020

Identification of multiple noise sources improves estimation of neural responses across stimulus conditions

Most models of neural responses are constructed to capture the average response to inputs but poorly reproduce the observed response variability. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system. Here, we present a new modeling framework that better captures observed features of neural response variability across stimulus conditions by incorporating multiple sources of noise. We use this model to fit responses of retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. The model reveals light level-dependent changes that could not be seen with previous models. It shows both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions. This modeling framework is general and can be applied to a variety of systems outside the retina.

neuroscience↗

bioRxiv2020

A high-density narrow-field inhibitory retinal interneuron with direct coupling to Müller glia

Amacrine cells are interneurons comprising the most diverse cell type in the mammalian retina. They help encode visual features such as edges or directed motion by mediating excitatory and inhibitory interactions between input (i.e. bipolar) and output (i.e. ganglion) neurons in the inner plexiform layer (IPL). Like other brain regions, the retina also contains glial cells that contribute to neurotransmitter uptake, neurovascular control and metabolic regulation. Here, we report that a previously poorly characterized, but relatively abundant, inhibitory amacrine cell type in the mouse retina is coupled directly to Muller glia. Electron microscopic reconstructions of this amacrine type revealed extensive associations with Muller glia, whose processes often completely ensheathe the neurites of this amacrine cell type. Microinjections of small tracer molecules into the somas of these amacrine cells led to selective labelling of nearby Muller glia, leading us to suggest the name "Muller glia-coupled amacrine cell" or MAC. Our electrophysiological data also indicate that MACs release glycine at conventional chemical synapses with amacrine, bipolar and retinal ganglion cells (RGCs), and viral transsynaptic tracing showed connections to several known RGC types. Visually-evoked responses revealed a strong preference for light increments; these "ON" responses were primarily mediated by excitatory chemical synaptic input and direct electrical coupling to other cells. This initial characterization of the MAC provides the first evidence for neuron-glia coupling in the mammalian retina and identifies the MAC as a potential link between inhibitory processing and glial function. Significance StatementGap junctions between pairs of neurons or glial cells are commonly found throughout the nervous system, and play a myriad of roles including electrical coupling and metabolic exchange. In contrast, gap junctions between neurons and glia cells are rare and poorly understood. Here we report the first evidence for neuron-glia coupling in the mammalian retina, specifically between an abundant (but previously unstudied) inhibitory interneuron and Muller glia.

neuroscience↗

bioRxiv2019

Rod-cone signal interference in the retina shapes perception in primates

Linking the activity of neurons, circuits and synapses to human behavior is a fundamental goal of neuroscience. Meeting this goal is challenging, in part because behavior, particularly perception, often masks the complexity of the underlying neural circuits, and in part because of the significant behavioral differences between primates and animals like mice and flies in which genetic manipulations are relatively common. Here we relate circuit-level processing of rod and cone signals in the non-human primate retina to a known break in the normal seamlessness of human vision - a surprising inability to see high contrast flickering lights under specific conditions. We use electrophysiological recordings and perceptual experiments to identify key mechanisms that shape the retinal integration of rod- and cone-generated retinal signals. We incorporate these mechanistic insights into a predictive model that accurately captures the cancellation of rod- and cone-mediated responses and can explain the perceptual insensitivity to flicker.

neuroscience↗

bioRxiv2019

Nonlinear convergence preserves information

Neural circuits are structured with layers of converging and diverging connectivity, and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered individually. These results show that the combination of selective nonlinearities and a convergent architecture - both elements that reduce information when acting separately - can promote efficient coding. Significance StatementComputation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about inputs. Past work has detailed how optimized nonlinearities and circuit weights can maximize information, but here, we show that incorporating non-invertible nonlinearities into a circuit with divergence and convergence, can enhance encoded information despite the suboptimality of these components individually. This study extends a broad literature on efficient coding to convergent circuits. Our results suggest that neural circuits may preserve more information using suboptimal components than one might expect.

neuroscience↗

bioRxiv2019

A computational observer model of spatial contrast sensitivity: Effects of photocurrent encoding, fixational eye movements and inference engine

We have recently shown that using the information carried by the mosaic of cone excitations of a stationary retina, the relative spatial contrast sensitivity function (CSF) of a computational observer has the same shape as a typical human subject. Absolute human sensitivity, however, is lower than the computational observer by a factor of 5 to 10. Here we model how additional known features of early vision affect spatial contrast sensitivity: fixational eye movements and the conversion of cone photopigment excitations to cone photocurrent responses. For a computational observer that uses a linear classifier applied to the responses of a stimulus-matched linear filter, fixational eye movements substantially change the shape of the spatial CSF, primarily by reducing sensitivity at spatial frequencies above 10 c/deg. For a computational observer that uses a translation-invariant calculation, in which decisions are based on the squared response of a quadrature-pair of linear filters, the CSF shape is little changed by eye movements, but there is a two-fold reduction in sensitivity. The noise and response dynamics of conversion of cone excitations into photocurrent introduce an additional two-fold sensitivity decrease. Hence, the combined effects of fixational eye movements and phototransduction bring the absolute sensitivity of the translation-invariant computational observer CSF to within a factor of 1 to 2 of the human CSF. We note that the human CSF depends on processing of the initial representation by many thalamic and cortical neurons, which are individually quite noisy. Our computational modeling suggests that the net effect of this noise on contrast-detection performance, when considered at the neural population level and behavioral level, is quite small: the inference mechanisms that determine the CSF, presumably in cortex, make efficient use of the information available from the cone photocurrents of the fixating eye.

neuroscience↗

bioRxiv2019

STAT pathway activation limits the Ascl1-mediated chromatin remodeling required for neural regeneration from Muller glia in adult mouse retina.

Muller glia can serve as a source for retinal regeneration in some non-mammalian vertebrates. Recently we found that this process can be induced in mouse Muller glia after injury, by combining transgenic expression of the proneural transcription factor Ascl1 and the HDAC inhibitor TSA. However, new neurons are only generated from a subset of Muller glia in this model, and identifying factors that limit Ascl1-mediated MG reprogramming could potentially make this process more efficient, and potentially useful clinically. One factor that limits neurogenesis in some non-mammalian vertebrates is the STAT pathway activation that occurs in Muller glia in response to injury. In this report, we tested whether injury induced STAT activation hampers the ability of Ascl1 to reprogram Muller glia into retinal neurons. Using a STAT inhibitor, in combination with our previously described reprogramming paradigm, we found a large increase in the ability of Muller glia to generate neurons, similar to those we described previously. Single-cell RNA-seq showed that the progenitor-like cells derived from Ascl1-expressing Muller glia have a higher level of STAT signaling than those that become neurons. Using Ascl1 ChIP-seq and DNase-seq, we found that developmentally inappropriate Ascl1 binding sites (that were unique to the overexpression context) had enrichment for the STAT binding motif. This study provides evidence that STAT pathway activation reduces the efficiency of Ascl1-mediated reprogramming in Muller glia, potentially by directing Ascl1 to inappropriate targets.

developmental biology↗

bioRxiv2018

Temporal resolution of single photon responses in primate rod photoreceptors and limits imposed by cellular noise

Sensory receptor noise corrupts sensory signals, contributing to imperfect perception and dictating central processing strategies. For example, noise in rod phototransduction limits our ability to detect light and minimizing the impact of this noise requires precisely tuned nonlinear processing by the retina. But detection sensitivity is only one aspect of night vision: prompt and accurate behavior also requires that rods reliably encode the timing of photon arrivals. We show here that the temporal resolution of responses of primate rods is much finer than the duration of the light response and identify the key limiting sources of transduction noise. We also find that the thermal activation rate of rhodopsin is lower than previous estimates, implying that other noise sources are more important than previously appreciated. A model of rod single-photon responses reveals that the limiting noise relevant for behavior depends critically on how rod signals are pooled by downstream neurons.

neuroscience↗

bioRxiv2018

Rod signaling in primate retina: range, routing and kinetics

Stimulus or context dependent routing of neural signals through parallel pathways can permit flexible processing of diverse inputs. For example, work in mouse shows that rod photoreceptor signals are routed through several retinal pathways, each specialized for different light levels. This light level-dependent routing of rod signals has been invoked to explain several human perceptual results, but it has not been tested in primate retina. Here we show, surprisingly, that rod signals traverse the primate retina almost exclusively through a single pathway, regardless of light level. Indeed, identical experiments in mouse and primate reveal large differences in how rod signals traverse the retina. These results require reevaluating human perceptual results in terms of flexible computation within this single pathway. This includes a prominent speeding of rod signals with light level - which we show is inherited directly from the rods photoreceptors themselves rather than from different pathways with different kinetics.

neuroscience↗

bioRxiv2018

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

A popular approach to the study of information processing in the nervous system is to char-acterize neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe the neurons stimulus integration properties, followed by a rectifying nonlinearity to convert filter output to spike rate. However, real neurons integrate stimuli via the modula-tion of nonlinear excitatory and inhibitory synaptic conductances. Here we introduce a bio-physically 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.

neuroscience↗

bioRxiv2018

Receptive field center-surround interactions mediate context-dependent spatial contrast encoding in the retina

Antagonistic receptive field surrounds are a near-universal property of early sensory processing. A key assumption in many models for retinal ganglion cell encoding is that receptive field surrounds are added only to the fully formed center signal. But anatomical and functional observations indicate that surrounds are added before the summation of signals across receptive field subunits that creates the center. Here, we show that this receptive field architecture has an important consequence for spatial contrast encoding: the surround can control sensitivity to fine spatial structure by changing the way the center integrates visual information over space. The impact of the surround is particularly prominent when center and surround signals are correlated, as they are in natural stimuli. This role of the surround differs substantially from classic center-surround models and raises the possibility that the surround plays unappreciated roles in shaping ganglion cell sensitivity to natural inputs.

neuroscience↗

bioRxiv2017

Simulation of visual perception and learning with a retinal prosthesis

The nature of artificial vision with a retinal prosthesis, and the degree to which the brain can adapt to the unnatural input from such a device, are poorly understood. Therefore, the development of current and future devices may be aided by theory and simulations that help to infer and understand what prosthesis patients see. A biologically-informed, extensible computational framework is presented here to predict visual perception and the potential effect of learning with a subretinal prosthesis. The framework relies on optimal linear reconstruction of the stimulus from retinal responses to infer the visual information available to the patient. A simulation of the physiological optics of the eye and light responses of the major retinal neurons was used to calculate the optimal linear transformation for reconstructing natural images from retinal activity. The result was then used to reconstruct the visual stimulus during the artificial activation expected from a subretinal prosthesis in a degenerated retina, as a proxy for inferred visual perception. Several simple observations reveal the potential utility of such a simulation framework. The inferred perception obtained with prosthesis activation was substantially degraded compared to the inferred perception obtained with normal retinal responses, as expected given the limited resolution and lack of cell type specificity of the prosthesis. Consistent with clinical findings and the importance of cell type specificity, reconstruction using only ON cells, and not OFF cells, was substantially more accurate. Finally, when reconstruction was re-optimized for prosthesis stimulation, simulating the greatest potential for learning by the patient, the accuracy of inferred perception was much closer to that of healthy vision. The reconstruction approach thus provides a more complete method for exploring the potential for treating blindness with retinal prostheses than has been available previously. It may also be useful for interpreting patient data in clinical trials, and for improving prosthesis design.

neuroscience↗

bioRxiv2017

Effective synaptic interactions in subsampled nonlinear networks with strong coupling

A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the \"hidden\" portion of the network. To properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuron r' to a post-synaptic neuron r can be decomposed into a sum of the true interaction from r' to r plus corrections from every directed path from r' to r through unobserved neurons. Importantly, the resulting formula reveals when the hidden units have--or do not have--major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with \"strong\" coupling--connection weights that scale with [Formula], where N is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.

neuroscience↗