Ancient origin of the rod bipolar cell pathway in the vertebrate retina

Vertebrates rely on rod photoreceptors for vision in low-light conditions. The specialized downstream circuit for rod signalling, called the primary rod pathway, is well characterized in mammals, but circuitry for rod signalling in non-mammals is largely unknown. Here we demonstrate that the mammalian primary rod pathway is conserved in zebrafish, which diverged from extant mammals ~400 million years ago. Using single-cell RNA sequencing, we identified two bipolar cell types in zebrafish that are related to mammalian rod bipolar cell (RBCs), the only bipolar type that directly carries rod signals from the outer to the inner retina in the primary rod pathway. By combining electrophysiology, histology and ultrastructural reconstruction of the zebrafish RBCs, we found that, similar to mammalian RBCs, both zebrafish RBC types connect with all rods in their dendritic territory and provide output largely onto amacrine cells. The wiring pattern of the amacrine cells postsynaptic to one RBC type is strikingly similar to that of mammalian RBCs and their amacrine partners, suggesting that the cell types and circuit design of the primary rod pathway emerged before the divergence of teleost fish and mammals. The second RBC type, which forms separate pathways, was either lost in mammals or emerged in fish.

Predictably manipulating photoreceptor light responses to reveal their role in downstream visual responses

Computation in neural circuits relies on judicious use of nonlinear circuit components. In many cases, multiple nonlinear components work collectively to control circuit outputs. Separating the contributions of these different components is difficult, and this hampers our understanding of the mechanistic basis of many important computations. Here, we introduce a tool that permits the design of light stimuli that predictably alter rod and cone phototransduction currents - including stimuli that compensate for nonlinear properties such as light adaptation. This tool, based on well-established models for the rod and cone phototransduction cascade, permits the separation of nonlinearities in phototransduction from those in downstream circuits. This will allow, for example, direct tests of how adaptation in rod and cone phototransduction affects downstream visual signals and perception.

ASCL1 induces neurogenesis in human Müller glia

In mammals, loss of retinal cells due to disease or trauma is an irreversible process that can lead to blindness. Interestingly, regeneration of retinal neurons is a well established process in some non-mammalian vertebrates and is driven by the Müller glia (MG), which are able to re-enter the cell cycle and reprogram into neurogenic progenitors upon retinal injury or disease. Progress has been made to restore this mechanism in mammals to promote retinal regeneration: MG can be stimulated to generate new neurons in vivo in the adult mouse retina after the over-expression of the pro-neural transcription factor Ascl1. In this study, we applied the same strategy to reprogram human MG derived from fetal retina and retinal organoids into neurons. Combining single cell RNA sequencing, single cell ATAC sequencing, immunofluorescence, and electrophysiology we demonstrate that human MG can be reprogrammed into neurogenic cells in vitro.

Poster Session II: Light-adaptation clamp: A tool to predictably manipulate photoreceptor light responses

Computation in neural circuits relies on judicious use of nonlinear circuit components. In many cases, multiple nonlinear components work collectively to control circuit outputs. Separating the contributions of these different components is difficult, and this hampers our understanding of the mechanistic basis of many important computations. Here, we introduce a tool that permits the design of light stimuli that predictably alter rod and cone phototransduction currents - including the compensation for nonlinear properties such as light adaptation. This tool, based on well-established models for the rod and cone phototransduction cascade, permits the separation of nonlinearities in phototransduction from those in downstream circuits. This will allow, for example, direct tests of the role of photoreceptor adaptation in downstream visual signals or in perception.

Ancient origin of the rod bipolar cell pathway in the vertebrate retina

Vertebrates rely on rod photoreceptors for vision in low-light conditions. Mammals have a specialized downstream circuit for rod signaling called the primary rod pathway, which comprises specific cell types and wiring patterns that are thought to be unique to this lineage. Thus, it has been long assumed that the primary rod pathway evolved in mammals. Here, we challenge this view by demonstrating that the mammalian primary rod pathway is conserved in zebrafish, which diverged from extant mammals ~400 million years ago. Using single-cell RNA-sequencing, we identified two bipolar cell (BC) types in zebrafish that are related to mammalian rod BCs (RBCs) of the primary rod pathway. By combining electrophysiology, histology, and ultrastructural reconstruction of the zebrafish RBCs, we found that, like mammalian RBCs, both zebrafish RBC types connect with all rods in their dendritic territory, and provide output largely onto amacrine cells. The wiring pattern of the amacrine cells post-synaptic to one RBC type is strikingly similar to that of mammalian RBCs, suggesting that the cell types and circuit design of the primary rod pathway have emerged before the divergence of teleost fish and amniotes. The second RBC type, which forms separate pathways, is either lost in mammals or emerged in fish.

Ancient origin of the rod bipolar cell pathway in the vertebrate retina

Vertebrates rely on rod photoreceptors for vision in low-light conditions1. Mammals have a specialized downstream circuit for rod signaling called the primary rod pathway, which comprises specific cell types and wiring patterns that are thought to be unique to this lineage2-6. Thus, it has been long assumed that the primary rod pathway evolved in mammals3,5-7. Here, we challenge this view by demonstrating that the mammalian primary rod pathway is conserved in zebrafish, which diverged from extant mammals ~400 million years ago. Using single-cell RNA-sequencing, we identified two bipolar cell (BC) types in zebrafish that are related to mammalian rod BCs (RBCs) of the primary rod pathway. By combining electrophysiology, histology, and ultrastructural reconstruction of the zebrafish RBCs, we found that, like mammalian RBCs8, both zebrafish RBC types connect with all rods and red-cones in their dendritic territory, and provide output largely onto amacrine cells. The wiring pattern of the amacrine cells post-synaptic to one RBC type is strikingly similar to that of mammalian RBCs. This suggests that the cell types and circuit design of the primary rod pathway may have emerged before the divergence of teleost fish and amniotes (mammals, bird, reptiles). The second RBC type in zebrafish, which forms separate pathways from the first RBC type, is either lost in mammals or emerged in fish to serve yet unknown roles.

Two Sides of the Same Coin: Efficient and Predictive Neural Coding

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

Light Adaptation of Retinal Rod Bipolar Cells

The sensitivity of retinal cells is altered in background light to optimize the detection of contrast. For scotopic (rod) vision, substantial adaptation occurs in the first two cells, the rods and rod bipolar cells (RBCs), through sensitivity adjustments in rods and postsynaptic modulation of the transduction cascade in RBCs. To study the mechanisms mediating these components of adaptation, we made whole-cell, voltage-clamp recordings from retinal slices of mice from both sexes. Adaptation was assessed by fitting the Hill equation to response-intensity relationships with the parameters of half-maximal response (I1/2 ), Hill coefficient (n), and maximum response amplitude (Rmax ). We show that rod sensitivity decreases in backgrounds according to the Weber-Fechner relation with an I1/2 of ∼50 R* s-1 The sensitivity of RBCs follows a near-identical function, indicating that changes in RBC sensitivity in backgrounds bright enough to adapt the rods are mostly derived from the rods themselves. Backgrounds too dim to adapt the rods can however alter n, relieving a synaptic nonlinearity likely through entry of Ca2+ into the RBCs. There is also a surprising decrease of Rmax , indicating that a step in RBC synaptic transduction is desensitized or that the transduction channels became reluctant to open. This effect is greatly reduced after dialysis of BAPTA at a membrane potential of +50 mV to impede Ca2+ entry. Thus the effects of background illumination in RBCs are in part the result of processes intrinsic to the photoreceptors and in part derive from additional Ca2+-dependent processes at the first synapse of vision.SIGNIFICANCE STATEMENT Light adaptation adjusts the sensitivity of vision as ambient illumination changes. Adaptation for scotopic (rod) vision is known to occur partly in the rods and partly in the rest of the retina from presynaptic and postsynaptic mechanisms. We recorded light responses of rods and rod bipolar cells to identify different components of adaptation and study their mechanisms. We show that bipolar-cell sensitivity largely follows adaptation of the rods but that light too dim to adapt the rods produces a linearization of the bipolar-cell response and a surprising decrease in maximum response amplitude, both mediated by a change in intracellular Ca2+ These findings provide a new understanding of how the retina responds to changing illumination.

Vision-Dependent and -Independent Molecular Maturation of Mouse Retinal Ganglion Cells

The development and connectivity of retinal ganglion cells (RGCs), the retina's sole output neurons, are patterned by activity-independent transcriptional programs and activity-dependent remodeling. To inventory the molecular correlates of these influences, we applied high-throughput single-cell RNA sequencing (scRNA-seq) to mouse RGCs at six embryonic and postnatal ages. We identified temporally regulated modules of genes that correlate with, and likely regulate, multiple phases of RGC development, ranging from differentiation and axon guidance to synaptic recognition and refinement. Some of these genes are expressed broadly while others, including key transcription factors and recognition molecules, are selectively expressed by one or a few of the 45 transcriptomically distinct types defined previously in adult mice. Next, we used these results as a foundation to analyze the transcriptomes of RGCs in mice lacking visual experience due to dark rearing from birth or to mutations that ablate either bipolar or photoreceptor cells. 98.5% of visually deprived (VD) RGCs could be unequivocally assigned to a single RGC type based on their transcriptional profiles, demonstrating that visual activity is dispensable for acquisition and maintenance of RGC type identity. However, visual deprivation significantly reduced the transcriptomic distinctions among RGC types, implying that activity is required for complete RGC maturation or maintenance. Consistent with this notion, transcriptomic alternations in VD RGCs significantly overlapped with gene modules found in developing RGCs. Our results provide a resource for mechanistic analyses of RGC differentiation and maturation, and for investigating the role of activity in these processes.

Reprogramming Müller glia to regenerate ganglion-like cells in adult mouse retina with developmental transcription factors

Many neurodegenerative diseases cause degeneration of specific types of neurons. For example, glaucoma leads to death of retinal ganglion cells, leaving other neurons intact. Neurons are not regenerated in the adult mammalian central nervous system. However, in nonmammalian vertebrates, glial cells spontaneously reprogram into neural progenitors and replace neurons after injury. We have recently developed strategies to stimulate regeneration of functional neurons in the adult mouse retina by overexpressing the proneural factor Ascl1 in Müller glia. Here, we test additional transcription factors (TFs) for their ability to direct regeneration to particular types of retinal neurons. We engineered mice to express different combinations of TFs in Müller glia, including Ascl1, Pou4f2, Islet1, and Atoh1. Using immunohistochemistry, single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and electrophysiology, we find that retinal ganglion-like cells can be regenerated in the damaged adult mouse retina in vivo with targeted overexpression of developmental retinal ganglion cell TFs.

Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs

The mammalian retina engages a broad array of linear and nonlinear circuit mechanisms to convert natural scenes into retinal ganglion cell (RGC) spike outputs. Although many individual integration mechanisms are well understood, we know less about how multiple mechanisms interact to encode the complex spatial features present in natural inputs. Here, we identified key spatial features in natural scenes that shape encoding by primate parasol RGCs. Our approach identified simplifications in the spatial structure of natural scenes that minimally altered RGC spike responses. We observed that reducing natural movies into 16 linearly integrated regions described ∼80% of the structure of parasol RGC spike responses; this performance depended on the number of regions but not their precise spatial locations. We used simplified stimuli to design high-dimensional metamers that recapitulated responses to naturalistic movies. Finally, we modeled the retinal computations that convert flashed natural images into one-dimensional spike counts.

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 computation. In the non-primate retina, direction selectivity arises in starburst amacrine cell (SAC) dendrites, which provide selective inhibition to direction-selective retinal ganglion cells (dsRGCs). Although SACs are present in primates, their connectivity and the existence of dsRGCs remain open questions. Here, we present a connectomic reconstruction of the primate ON SAC circuit from a serial electron microscopy volume of the macaque central retina. We show that the structural basis for the SACs' ability to confer directional selectivity on postsynaptic neurons is conserved. SACs selectively target a candidate homolog to the mammalian ON-sustained dsRGCs that project to the accessory optic system (AOS) and contribute to gaze-stabilizing reflexes. These results indicate that the capacity to compute motion direction is present in the retina, which is earlier in the primate visual system than classically thought.

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of primate 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 nonlinear circuit behavior is fundamental to neural computation. Here, we study a surprising stimulus dependence of the responses of macaque On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in some stimuli but near linearly to spatial structure in others, including natural inputs. We show that these differences in the linearity of the integration of spatial inputs can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least partially 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.

Retinal encoding of natural images

Current predictive models of natural scene encoding by retinal ganglion cells perform poorly, especially when tested on stimuli that they are not trained on. This failure is likely to originate at least in part because the functional architecture (e.g. locations of linear and nonlinear components) of these models does not match that of real retinal circuits. We take an empirical approach to simplify the structure of natural scenes. We find that reducing natural movies to 16 linearly integrated regions describes ~80% of the structure of parasol RGC spike responses. We test our understanding by using these simplified stimuli to create high-dimensional metamers that recapitulate the spike response of full-field naturalistic movies. Finally, we identify the retinal computations that convert natural images in 16-dimensional space into 1-dimensional spike outputs. This work provides a description of spatial integration that generalizes well across images and that corresponds closely to known anatomical and functional aspects of retinal circuits.\.

Efficient stimulation of retinal regeneration from Müller glia in adult mice using combinations of proneural bHLH transcription factors

Regenerative neuroscience aims to stimulate endogenous repair in the nervous system to replace neurons lost from degenerative diseases. Recently, we reported that overexpressing the transcription factor Ascl1 in Müller glia (MG) is sufficient to stimulate MG to regenerate functional neurons in the adult mouse retina. However, this process is inefficient, and only a third of the Ascl1-expressing MG generate new neurons. Here, we test whether proneural transcription factors of the Atoh1/7 class can further promote the regenerative capacity of MG. We find that the combination of Ascl1:Atoh1 is remarkably efficient at stimulating neurogenesis, even in the absence of retinal injury. Using electrophysiology and single-cell RNA sequencing (scRNA-seq), we demonstrate that Ascl1:Atoh1 generates a diversity of retinal neuron types, with the majority expressing characteristics of retinal ganglion cells. Our results provide a proof of principle that combinations of developmental transcription factors can substantially improve glial reprogramming to neurons and expand the repertoire of regenerated cell fates.

Transient expression of a GABA receptor subunit during early development is critical for inhibitory synapse maturation and function

Developing neural circuits, including GABAergic circuits, switch receptor types. But the role of early GABA receptor expression for establishment of functional inhibitory circuits remains unclear. Tracking the development of GABAergic synapses across axon terminals of retinal bipolar cells (BCs), we uncovered a crucial role of early GABA_A receptor expression for the formation and function of presynaptic inhibitory synapses. Specifically, early α3-subunit-containing GABA_A (GABA_Aα3) receptors are a key developmental organizer. Before eye opening, GABA_Aα3 gives way to GABA_Aα1 at individual BC presynaptic inhibitory synapses. The developmental downregulation of GABA_Aα3 is independent of GABA_Aα1 expression. Importantly, lack of early GABA_Aα3 impairs clustering of GABA_Aα1 and formation of functional GABA_A synapses across mature BC terminals. This impacts the sensitivity of visual responses transmitted through the circuit. Lack of early GABA_Aα3 also perturbs aggregation of LRRTM4, the organizing protein at GABAergic synapses of rod BC terminals, and their arrangement of output ribbon synapses.

A High-Density Narrow-Field Inhibitory Retinal Interneuron with Direct Coupling to Müller Glia

Amacrine cells are interneurons composing the most diverse cell class 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, metabolic regulation, and neurovascular control. Here, we report that, in mouse retina (of either sex), an abundant, though previously unstudied inhibitory amacrine cell is coupled directly to Müller glia. Electron microscopic reconstructions of this amacrine type revealed chemical synapses with known retinal cell types and extensive associations with Müller glia, the processes of which often completely ensheathe the neurites of this amacrine cell. Microinjecting small tracer molecules into the somas of these amacrine cells led to selective labeling of nearby Müller glia, leading us to suggest the name "Müller glia-coupled amacrine cell," or MAC. Our data also indicate that MACs release glycine at conventional chemical synapses, and viral retrograde transsynaptic tracing from the dorsal lateral geniculate nucleus showed selective connections between MACs and a subpopulation of retinal ganglion cell 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 with 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 STATEMENT Gap junctions between pairs of neurons or glial cells are commonly found throughout the nervous system and play multiple roles, including electrical coupling and metabolic exchange. In contrast, gap junctions between neurons and glia cells have rarely been reported and are 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 Müller glia. Moreover, viral tracing, optogenetics, and serial electron microscopy provide new information about the neuron's synaptic partners and physiological responses.

Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions

Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.

The Geometry of Information Coding in Correlated Neural Populations

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.

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

We have recently shown that the relative spatial contrast sensitivity function (CSF) of a computational observer operating on the cone mosaic photopigment excitations of a stationary retina has the same shape as human subjects. Absolute human sensitivity, however, is 5- to 10-fold lower than the computational observer. Here we model how additional known features of early vision affect the CSF: fixational eye movements and the conversion of cone photopigment excitations to cone photocurrents (phototransduction). 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 CSF by reducing sensitivity above 10 c/deg. For a translation-invariant computational observer that operates 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. Phototransduction dynamics introduce an additional two fold sensitivity decrease. Hence, the combined effects of fixational eye movements and phototransduction bring the absolute CSF of the translation-invariant computational observer to within a factor of 1 to 2 of the human CSF. We note that the human CSF depends on processing of the retinal representation by many thalamo-cortical neurons, which are individually quite noisy. Our modeling suggests that the net effect of post-retinal noise on contrast-detection performance, when considered at the neural population and behavioral level, is quite small: The inference mechanisms that determine the CSF, presumably in cortex, make efficient use of the information carried by the cone photocurrents of the fixating eye.

Nonlinear convergence boosts information coding in circuits with parallel outputs

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 separately.

STAT Signaling Modifies Ascl1 Chromatin Binding and Limits Neural Regeneration from Muller Glia in Adult Mouse Retina

Müller glia (MG) serve as sources for retinal regeneration in non-mammalian vertebrates. We find that this process can be induced in mouse MG, after injury, by transgenic expression of the proneural transcription factor Ascl1 and the HDAC inhibitor TSA. However, new neurons are generated only from a subset of MG. Identifying factors that limit Ascl1-mediated MG reprogramming could make this process more efficient. In this study, we test whether injury-induced STAT activation hampers the ability of Ascl1 to reprogram MG into retinal neurons. Single-cell RNA-seq shows that progenitor-like cells derived from Ascl1-expressing MG have a higher level of STAT signaling than do those cells that become neurons. Ascl1-ChIPseq and ATAC-seq show that STAT potentially directs Ascl1 to developmentally inappropriate targets. Using a STAT inhibitor, in combination with our previously described reprogramming paradigm, we found a large increase in the ability of MG to generate neurons.

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

Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes a mapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.

Dynamic assembly of ribbon synapses and circuit maintenance in a vertebrate sensory system

Ribbon synapses transmit information in sensory systems, but their development is not well understood. To test the hypothesis that ribbon assembly stabilizes nascent synapses, we performed simultaneous time-lapse imaging of fluorescently-tagged ribbons in retinal cone bipolar cells (BCs) and postsynaptic densities (PSD95-FP) of retinal ganglion cells (RGCs). Ribbons and PSD95-FP clusters were more stable when these components colocalized at synapses. However, synapse density on ON-alpha RGCs was unchanged in mice lacking ribbons (ribeye knockout). Wildtype BCs make both ribbon-containing and ribbon-free synapses with these GCs even at maturity. Ribbon assembly and cone BC-RGC synapse maintenance are thus regulated independently. Despite the absence of synaptic ribbons, RGCs continued to respond robustly to light stimuli, although quantitative examination of the responses revealed reduced frequency and contrast sensitivity.

S-cone photoreceptors in the primate retina are functionally distinct from L and M cones

Daylight vision starts with signals in three classes of cone photoreceptors sensitive to short (S), middle (M), and long (L) wavelengths. Psychophysical studies show that perceptual sensitivity to rapidly varying inputs differs for signals originating in S cones versus L and M cones; notably, S-cone signals appear perceptually delayed relative to L- and M-cone signals. These differences could originate in the cones themselves or in the post-cone circuitry. To determine if the cones could contribute to these and related perceptual phenomena, we compared the light responses of primate S, M, and L cones. We found that S cones generate slower light responses than L and M cones, show much smaller changes in response kinetics as background-light levels increase, and are noisier than L and M cones. It will be important to incorporate these differences into descriptions of how cone signaling shapes human visual perception.

Stimulus- and goal-oriented frameworks for understanding natural vision

Our knowledge of sensory processing has advanced dramatically in the last few decades, but this understanding remains far from complete, especially for stimuli with the large dynamic range and strong temporal and spatial correlations characteristic of natural visual inputs. Here we describe some of the issues that make understanding the encoding of natural images a challenge. We highlight two broad strategies for approaching this problem: a stimulus-oriented framework and a goal-oriented one. Different contexts can call for one framework or the other. Looking forward, recent advances, particularly those based in machine learning, show promise in borrowing key strengths of both frameworks and by doing so illuminating a path to a more comprehensive understanding of the encoding of natural stimuli.

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. NEW & NOTEWORTHY Many studies have focused on the visual system's ability to detect photons, but not on its ability to encode the relative timing of detected photons. Timing is essential for computations such as determining the velocity of moving objects. Here we examine the timing precision of primate rod photoreceptor responses and show that it is more precise than previously appreciated. This motivates an evaluation of whether scotopic vision approaches limits imposed by rod temporal resolution.

Simulation of visual perception and learning with a retinal prosthesis

OBJECTIVE

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.

APPROACH

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.

MAIN RESULTS

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.

SIGNIFICANCE

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.

Predicting how and when hidden neurons skew measured synaptic interactions

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 1/N, 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.

No experiment in neuroscience can record from more than a tiny fraction of the total number of neurons present in a circuit. This severely complicates measurement of a network’s true properties, as unobserved neurons skew measurements away from what would be measured if all neurons were observed. For example, the measured post-synaptic response of a neuron to a spike from a particular pre-synaptic neuron incorporates direct connections between the two neurons as well as the effect of any number of indirect connections, including through unobserved neurons. To understand how measured quantities are distorted by unobserved neurons, we calculate a general relationship between measured “effective” synaptic interactions and the ground-truth interactions in the network. This allows us to identify conditions under which hidden neurons substantially alter measured interactions. Moreover, it provides a foundation for future work on manipulating effective interactions between neurons to better understand and potentially alter circuit function—or dysfunction.

Range, routing and kinetics of rod signaling in primate retina

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 – the dedicated rod bipolar pathway. Identical experiments in mouse and primate reveal substantial 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 rod photoreceptors themselves rather than from different pathways with distinct kinetics.

Parallel Processing of Rod and Cone Signals: Retinal Function and Human Perception

We know a good deal about the operation of the retina when either rod or cone photoreceptors provide the dominant input (i.e., under very dim or very bright conditions). However, we know much less about how the retina operates when rods and cones are coactive (i.e., under intermediate lighting conditions, such as dusk). Such mesopic conditions span 20-30% of the light levels over which vision operates and encompass many situations in which vision is essential (e.g., driving at night). These lighting conditions are challenging because rod and cone signals differ substantially: Rod responses are nearing saturation, while cone responses are weak and noisy. A rich history of perceptual studies guides our investigation of how the retina operates under mesopic conditions and in doing so provides a powerful opportunity to link general issues about parallel processing in neural circuits with computation and perception. We review some of the successes and challenges in understanding the retinal basis of perceptual rod-cone interactions.

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 in the macaque monkey retina: 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 effect 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.

Contrast-dependent red-green hue shift

On bright surrounds, red-green-balanced yellow targets become greenish brown with decreased target luminance, and red-green-balanced brown targets become reddish yellow with increased target luminance. These effects imply luminance- and/or contrast-dependent weighting of M- and L-cone signals in post-receptoral pathways. We show psychophysically that luminance contrast between the surround and the target is the primary determinant of the magnitude of red-green hue shift, requiring surround luminance at least twice the target luminance and increasing with further increases of surround/target contrast. There is a much smaller effect of absolute stimulus luminance, with dimmer stimuli showing slightly larger hue shifts. To evaluate a possible retinal origin of the changes in cone-signal weightings underlying the hue shift, we recorded spike responses from both ON- and OFF-center midget ganglion cells in peripheral primate retina. We found no evidence that the relative strength of L- and M-cone post-receptoral responses changed systematically with change of surround irradiance. Nor was there any systematic difference between ON- and OFF-subtypes. This suggests that the change in cone signal weighting occurs later in the visual system.

Development of ON and OFF cholinergic amacrine cells in the human fetal retina

Choline acetyltransferase (ChAT) expressing retinal amacrine cells are present across vertebrates. These interneurons play important roles in the development of retinal projections to the brain and in motion detection, specifically in generating direction-selective responses to moving stimuli. ChAT amacrine cells typically comprise two spatially segregated populations that form circuits in the 'ON' or 'OFF' synaptic layers of the inner retina. This stereotypic arrangement is also found across the adult human retina, with the notable exception that ChAT expression is evident in the ON but not OFF layer of the fovea, a region specialized for high-acuity vision. We thus investigated whether the human fovea exhibits a developmental path for ON and OFF ChAT cells that is retinal location-specific. Our analysis shows that at each retinal location, human ON and OFF ChAT cells differentiate, form their separate synaptic layers, and establish non-random mosaics at about the same time. However, unlike in the adult fovea, ChAT immunostaining is initially robust in both ON and OFF populations, up until at least mid-gestation. ChAT expression in the OFF layer in the fovea is therefore significantly reduced after mid-gestation. OFF ChAT cells in the human fovea and in the retinal periphery thus follow distinct maturational paths.

Flexible Neural Hardware Supports Dynamic Computations in Retina

The ability of the retina to adapt to changes in mean light intensity and contrast is well known. Classically, however, adaptation is thought to affect gain but not to change the visual modality encoded by a given type of retinal neuron. Recent findings reveal unexpected dynamic properties in mouse retinal neurons that challenge this view. Specifically, certain cell types change the visual modality they encode with variations in ambient illumination or following repetitive visual stimulation. These discoveries demonstrate that computations performed by retinal circuits with defined architecture can change with visual input. Moreover, they pose a major challenge for central circuits that must decode properties of the dynamic visual signal from retinal outputs.

Stimulation of functional neuronal regeneration from Müller glia in adult mice

Many retinal diseases lead to the loss of retinal neurons and cause visual impairment. The adult mammalian retina has little capacity for regeneration. By contrast, teleost fish functionally regenerate their retina following injury, and Müller glia (MG) are the source of regenerated neurons. The proneural transcription factor Ascl1 is upregulated in MG after retinal damage in zebrafish and is necessary for regeneration. Although Ascl1 is not expressed in mammalian MG after injury, forced expression of Ascl1 in mouse MG induces a neurogenic state in vitro and in vivo after NMDA (N-methyl-d-aspartate) damage in young mice. However, by postnatal day 16, mouse MG lose neurogenic capacity, despite Ascl1 overexpression. Loss of neurogenic capacity in mature MG is accompanied by reduced chromatin accessibility, suggesting that epigenetic factors limit regeneration. Here we show that MG-specific overexpression of Ascl1, together with a histone deacetylase inhibitor, enables adult mice to generate neurons from MG after retinal injury. The MG-derived neurons express markers of inner retinal neurons, synapse with host retinal neurons, and respond to light. Using an assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), we show that the histone deacetylase inhibitor promotes accessibility at key gene loci in the MG, and allows more effective reprogramming. Our results thus provide a new approach for the treatment of blinding retinal diseases.

Cellular and Circuit Mechanisms Shaping the Perceptual Properties of the Primate Fovea

The fovea is a specialized region of the retina that dominates the visual perception of primates by providing high chromatic and spatial acuity. While the foveal and peripheral retina share a similar core circuit architecture, they exhibit profound functional differences whose mechanisms are unknown. Using intracellular recordings and structure-function analyses, we examined the cellular and synaptic underpinnings of the primate fovea. Compared to peripheral vision, the fovea displays decreased sensitivity to rapid variations in light inputs; this difference is reflected in the responses of ganglion cells, the output cells of the retina. Surprisingly, and unlike in the periphery, synaptic inhibition minimally shaped the responses of foveal midget ganglion cells. This difference in inhibition cannot however, explain the differences in the temporal sensitivity of foveal and peripheral midget ganglion cells. Instead, foveal cone photoreceptors themselves exhibited slower light responses than peripheral cones, unexpectedly linking cone signals to perceptual sensitivity.

Brightness in human rod vision depends on slow neural adaptation to quantum statistics of light

In human rod-mediated vision, threshold for small, brief flashes rises in proportion to the square root of adapting luminance at all but the lowest and highest adapting intensities. A classical signal detection theory from Rose (1942, 1948) and de Vries (1943) attributed this rise to the perceptual masking of weak flashes by Poisson fluctuations in photon absorptions from the adapting field. However, previous work by Brown and Rudd (1998) demonstrated that the square-root law also holds for suprathreshold brightness judgments, a finding that supports an alternative explanation of the square-root sensitivity changes as a consequence of physiological adaptation (i.e., neural gain control). Here, we employ a dichoptic matching technique to investigate the properties of this brightness gain control. We show that the brightness gain control: 1) affects the brightness of high-intensity suprathreshold flashes for which assumptions of the de Vries-Rose theory are strongly violated; 2) exhibits a long time course of 100-200 s; and 3) is subject to modulation by temporal contrast noise when the mean adapting luminance is held constant. These findings are consistent with the hypothesis that the square-root law results from a slow neural adaptation to statistical noise in the rod pool. We suggest that such adaptation may function to reduce the probability of spurious ganglion cell spiking activity due to photon fluctuation noise as the ambient illumination level is increased.

How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

25th Annual Computational Neuroscience Meeting: CNS-2016

A1 Functional advantages of cell-type heterogeneity in neural circuits

Tatyana O. Sharpee

A2 Mesoscopic modeling of propagating waves in visual cortex

Alain Destexhe

A3 Dynamics and biomarkers of mental disorders

Mitsuo Kawato

F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons

Vladislav Sekulić, Frances K. Skinner

F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains

Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári

F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.

Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić

O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators

Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona

O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain

Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi

O3 Modeling auditory stream segregation, build-up and bistability

James Rankin, Pamela Osborn Popp, John Rinzel

O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields

Alejandro Tabas, André Rupp, Emili Balaguer-Ballester

O5 A simple model of retinal response to multi-electrode stimulation

Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin

O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task

Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer

O7 Input-location dependent gain modulation in cerebellar nucleus neurons

Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber

O8 Analytic solution of cable energy function for cortical axons and dendrites

Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu

O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network

Jimin Kim, Will Leahy, Eli Shlizerman

O10 Is the model any good? Objective criteria for computational neuroscience model selection

Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook

O11 Cooperation and competition of gamma oscillation mechanisms

Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen

O12 A discrete structure of the brain waves

Yuri Dabaghian, Justin DeVito, Luca Perotti

O13 Direction-specific silencing of the Drosophila gaze stabilization system

Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon

O14 What does the fruit fly think about values? A model of olfactory associative learning

Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn

O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)

Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll

O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits

Yasunori Yamada

O17 Spatial coarse-graining the brain: origin of minicolumns

Moira L. Steyn-Ross, D. Alistair Steyn-Ross

O18 Modeling large-scale cortical networks with laminar structure

Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang

O19 Information filtering by partial synchronous spikes in a neural population

Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner

O20 Decoding context-dependent olfactory valence in Drosophila

Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama

P1 Neural network as a scale-free network: the role of a hub

B. Kahng

P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging

Nicoladie D. Tam

P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique

Nicoladie D.Tam, Luca Pollonini, George Zouridakis

P4 Modeling jamming avoidance of weakly electric fish

Jaehyun Soh, DaeEun Kim

P5 Synergy and redundancy of retinal ganglion cells in prediction

Minsu Yoo, S. E. Palmer

P6 A neural field model with a third dimension representing cortical depth

Viviana Culmone, Ingo Bojak

P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord

Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk

P8 The recognition dynamics in the brain

Chang Sub Kim

P9 Multivariate spike train analysis using a positive definite kernel

Taro Tezuka

P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia

Pangyu Joo

P11 The ionic basis of heterogeneity affects stochastic synchrony

Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban

P12 Circular statistics of noise in spike trains with a periodic component

Petr Marsalek

P14 Representations of directions in EEG-BCI using Gaussian readouts

Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong

P15 Action selection and reinforcement learning in basal ganglia during reaching movements

Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak

P17 Axon guidance: modeling axonal growth in T-Junction assay

Csaba Forro, Harald Dermutz, László Demkó, János Vörös

P19 Transient cell assembly networks encode persistent spatial memories

Yuri Dabaghian, Andrey Babichev

P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons

Haiping Huang

P21 Design of biologically-realistic simulations for motor control

Sergio Verduzco-Flores

P22 Towards understanding the functional impact of the behavioural variability of neurons

Filipa Dos Santos, Peter Andras

P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia

Christoph Metzner, Achim Schweikard, Bartosz Zurowski

P24 Memory recall and spike frequency adaptation

James P. Roach, Leonard M. Sander, Michal R. Zochowski

P25 Stability of neural networks and memory consolidation preferentially occur near criticality

Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski

P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou

P27 Neurofield: a C++ library for fast simulation of 2D neural field models

Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao

P28 Action-based grounding: Beyond encoding/decoding in neural code

Yoonsuck Choe, Huei-Fang Yang

P29 Neural computation in a dynamical system with multiple time scales

Yuanyuan Mi, Xiaohan Lin, Si Wu

P30 Maximum entropy models for 3D layouts of orientation selectivity

Joscha Liedtke, Manuel Schottdorf, Fred Wolf

P31 A behavioral assay for probing computations underlying curiosity in rodents

Yoriko Yamamura, Jeffery R. Wickens

P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models

Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver

P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm

Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang

P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis

Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon

P35 Dynamics of cooperative excitatory and inhibitory plasticity

Everton J. Agnes, Tim P. Vogels

P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons

William F. Podlaski, Tim P. Vogels

P37 Phenomenological neural model for adaptation of neurons in area IT

Martin Giese, Pradeep Kuravi, Rufin Vogels

P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment

Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels

P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations

Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona

P40 Different roles for transient and sustained activity during active visual processing

Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden

P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications

Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh

P42 High frequency neuron can facilitate propagation of signal in neural networks

Aref Pariz, Shervin S. Parsi, Alireza Valizadeh

P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus

Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova

P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP

Florence I. Kleberg, Jochen Triesch

P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex

Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch

P46 Structure and dynamics of axon network formed in primary cell culture

Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau

P47 Efficient signal processing and sampling in random networks that generate variability

Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi

P48 Modeling the effect of riluzole on bursting in respiratory neural networks

Daniel T. Robb, Nick Mellen, Natalia Toporikova

P49 Mapping relaxation training using effective connectivity analysis

Rongxiang Tang, Yi-Yuan Tang

P50 Modeling neuron oscillation of implicit sequence learning

Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang

P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus

Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber

P52 Nonlinear response of noisy neurons

Sergej O. Voronenko, Benjamin Lindner

P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion

Tosif Ahamed, Greg Stephens

P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings

Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre

P55 Sufficient sampling rates for fast hand motion tracking

Hansol Choi, Min-Ho Song

P56 Linear readout of object manifolds

SueYeon Chung, Dan D. Lee, Haim Sompolinsky

P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves

Ryan S. Phillips, Jeffrey Smith

P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus

Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner

P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning

N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver

P60 A set of curated cortical models at multiple scales on Open Source Brain

Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver

P61 A synaptic story of dynamical information encoding in neural adaptation

Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu

P62 Physical modeling of rule-observant rodent behavior

Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin

P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes

Hannah Choi, Anitha Pasupathy, Eric Shea-Brown

P65 Stability of FORCE learning on spiking and rate-based networks

Dongsung Huh, Terrence J. Sejnowski

P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments

Simon M. Vogt, Arvind Kumar, Robert Schmidt

P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation

Stephen Van Wert, Steven J. Schiff

P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo

Richard Veale, Matthias Scheutz

P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions

Sang Wan Lee

P70 Maturation of sensory networks through homeostatic structural plasticity

Júlia Gallinaro, Stefan Rotter

P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings

Paula Sanz-Leon, Peter A. Robinson

P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study

Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan

P73 Exact spike-timing distribution reveals higher-order interactions of neurons

Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli

P74 Neural mechanism of visual perceptual learning using a multi-layered neural network

Xiaochen Zhao, Malte J. Rasch

P75 Inferring collective spiking dynamics from mostly unobserved systems

Jens Wilting, Viola Priesemann

P76 How to infer distributions in the brain from subsampled observations

Anna Levina, Viola Priesemann

P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons

Lucas Rudelt, Joseph T. Lizier, Viola Priesemann

P78 A nearest-neighbours based estimator for transfer entropy between spike trains

Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann

P79 Active learning of psychometric functions with multinomial logistic models

Ji Hyun Bak, Jonathan Pillow

P81 Inferring low-dimensional network dynamics with variational latent Gaussian process

Yuan Zaho, Il Memming Park

P82 Computational investigation of energy landscapes in the resting state subcortical brain network

Jiyoung Kang, Hae-Jeong Park

P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map

Jaeson Jang, Se-Bum Paik

P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition

Woochul Choi, Se-Bum Paik

P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map

Changju Lee, Jaeson Jang, Se-Bum Paik

P86 Computational method classifying neural network activity patterns for imaging data

Min Song, Hyeonsu Lee, Se-Bum Paik

P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory

Youngjin Park, Woochul Choi^, Se-Bum Paik

P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons

Ergin Yilmaz, Veli Baysal, Mahmut Ozer

P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance

Veronika Koren, Klaus Obermayer

P90 Methods for building accurate models of individual neurons

Daniel Saska, Thomas Nowotny

P91 A full size mathematical model of the early olfactory system of honeybees

Ho Ka Chan, Alan Diamond, Thomas Nowotny

P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks

Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre

P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input

Philipp Weidel, Renato Duarte, Abigail Morrison

P94 Modulation of tuning induced by abrupt reduction of SST cell activity

Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas

P95 The functional role of VIP cell activation during locomotion

Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas

P96 Stochastic inference with spiking neural networks

Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier

P97 Modeling orientation-selective electrical stimulation with retinal prostheses

Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin

P98 Ion channel noise can explain firing correlation in auditory nerves

Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell

P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit

Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram

P100 On the representation of arm reaching movements: a computational model

Antonio Parziale, Rosa Senatore, Angelo Marcelli

P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior

Rosa Senatore, Antonio Parziale, Angelo Marcelli

P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge

K. Skiker, M. Maouene

P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia

Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton

P104 Effect of network size on computational capacity

Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr

P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks

Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton

P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas

Juhyoung Ryu, Sang-Hun Lee

P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception

Joonwon Lee, Sang-Hun Lee

P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making

Hyang Jung Lee, Sang-Hun Lee

P110 A Bayesian algorithm for phoneme Perception and its neural implementation

Daeseob Lim, Sang-Hun Lee

P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol

Jisung Wang, Heonsoo Lee

P112 Self-organized criticality of neural avalanche in a neural model on complex networks

Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee

P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model

Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee

P114 Computational model to replicate seizure suppression effect by electrical stimulation

Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee

P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy

Felix Goetze, Pik-Yin Lai

P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell

Seonghyun Kim, Jeehyun Kwag

P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model

Hyun Jae Jang, Jeehyun Kwag

P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum

Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar

P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease

Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison

P120 Learning sparse representations in the olfactory bulb

Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov

P121 Functional classification of homologous basal-ganglia networks

Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski

P122 Short term memory based on multistability

Tim Kunze, Andre Peterson, Thomas Knösche

P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units

Minjung Kim, Hojeong Kim

P125 Decoding laser-induced somatosensory information from EEG

Ji Sung Park, Ji Won Yeon, Sung-Phil Kim

P126 Phase synchronization of alpha activity for EEG-based personal authentication

Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim

P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model

Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa

P130 Epileptic seizures in the unfolding of a codimension-3 singularity

Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa

P131 Incremental dimensional exploratory reasoning under multi-dimensional environment

Oh-hyeon Choung, Yong Jeong

P132 A low-cost model of eye movements and memory in personal visual cognition

Yong-il Lee, Jaeseung Jeong

P133 Complex network analysis of structural connectome of autism spectrum disorder patients

Su Hyun Kim, Mir Jeong, Jaeseung Jeong

P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip

Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong

P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making

Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong

P136 Detecting purchase decision based on hyperfrontality of the EEG

Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong

P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome

Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong

P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans

Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong

P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?

Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong

P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish

Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa

P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data

JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii

P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems

Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta

P146 Swinging networks

Michal Hadrava, Jaroslav Hlinka

P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions

Hannah Bos, Moritz Helias

P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning

Charles M. Welzig, Zachary J. Harper

P149 Multiscale complexity analysis for the segmentation of MRI images

Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han

P150 A neuro-computational model of emotional attention

René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker

P151 Multi-site delayed feedback stimulation in parkinsonian networks

Kelly Toppin, Yixin Guo

P152 Bistability in Hodgkin–Huxley-type equations

Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden

P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency

Mark D. McDonnell, Bruce P. Graham

P154 Quantifying resilience patterns in brain networks: the importance of directionality

Penelope J. Kale, Leonardo L. Gollo

P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations

Merav Stern, L. F. Abbott

P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues

Leonid A. Fedorov, Martin A. Giese

P157 Spiking model for the interaction between action recognition and action execution

Mohammad Hovaidi Ardestani, Martin Giese

P158 Surprise-modulated belief update: how to learn within changing environments?

Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner

P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation

Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns

P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks

Won Hee Lee, Sophia Frangou

P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis

Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito

P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations

Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink

P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse

Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers

P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes

Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst

P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology

Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll

P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach

Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen

P167 Local field potentials in a 4 × 4 mm² multi-layered network model

Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann

P168 A spiking network model explains multi-scale properties of cortical dynamics

Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada

P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups

Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk

P170 Tensor decomposition reveals RSNs in simulated resting state fMRI

Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco

P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening

Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach^, Peter Vuust

P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors

Guido Klingbeil, Erik De Schutter

P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS

Weiliang Chen, Erik De Schutter

P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input

Yunliang Zang, Erik De Schutter

P175 Dendritic morphology determines how dendrites are organized into functional subunits

Sungho Hong, Akira Takashima, Erik De Schutter

P176 A model of Ca²⁺/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells

Criseida Zamora, Andrew R. Gallimore, Erik De Schutter

P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents

Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta

P178 Data-driven neural models part II: connectivity patterns of human seizures

Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook

P179 Data-driven neural models part I: state and parameter estimation

Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook

P180 Spectral and spatial information processing in human auditory streaming

Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen

P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain

Leonardo L. Gollo, James A. Roberts, Luca Cocchi

P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles

Yann Sweeney, Claudia Clopath

P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography

Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi

P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep

Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi

P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response

Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi

P186 Neural field model of localized orientation selective activation in V1

James Rankin, Frédéric Chavane

P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia

Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P189 A computational architecture to model the microanatomy of the striatum and its functional properties

Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy

P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching

Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy

P191 Emergence of radial orientation selectivity from synaptic plasticity

Catherine E. Davey, David B. Grayden, Anthony N. Burkitt

P192 How do hidden units shape effective connections between neurons?

Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice

P193 Characterization of neural firing in the presence of astrocyte-synapse signaling

Maurizio De Pittà, Hugues Berry, Nicolas Brunel

P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics

James A. Roberts, Leonardo L. Gollo, Michael Breakspear

P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings

Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley

P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses

Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell

P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons

LieJune Shiau, Laure Buhry, Kanishka Basnayake

P200 Visual face representations during memory retrieval compared to perception

Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker

P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics

Timothée Leleu, Kazuyuki Aihara

Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics

Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy

Glutamatergic Monopolar Interneurons Provide a Novel Pathway of Excitation in the Mouse Retina

Excitatory and inhibitory neurons in the CNS are distinguished by several features, including morphology, transmitter content, and synapse architecture [1]. Such distinctions are exemplified in the vertebrate retina. Retinal bipolar cells are polarized glutamatergic neurons receiving direct photoreceptor input, whereas amacrine cells are usually monopolar inhibitory interneurons with synapses almost exclusively in the inner retina [2]. Bipolar but not amacrine cell synapses have presynaptic ribbon-like structures at their transmitter release sites. We identified a monopolar interneuron in the mouse retina that resembles amacrine cells morphologically but is glutamatergic and, unexpectedly, makes ribbon synapses. These glutamatergic monopolar interneurons (GluMIs) do not receive direct photoreceptor input, and their light responses are strongly shaped by both ON and OFF pathway-derived inhibitory input. GluMIs contact and make almost as many synapses as type 2 OFF bipolar cells onto OFF-sustained A-type (AOFF-S) retinal ganglion cells (RGCs). However, GluMIs and type 2 OFF bipolar cells possess functionally distinct light-driven responses and may therefore mediate separate components of the excitatory synaptic input to AOFF-S RGCs. The identification of GluMIs thus unveils a novel cellular component of excitatory circuits in the vertebrate retina, underscoring the complexity in defining cell types even in this well-characterized region of the CNS.

Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code

Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent, and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction 2-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons.

Synaptic Rectification Controls Nonlinear Spatial Integration of Natural Visual Inputs

A central goal in the study of any sensory system is to predict neural responses to complex inputs, especially those encountered during natural stimulation. Nowhere is the transformation from stimulus to response better understood than the vertebrate retina. Nevertheless, descriptions of retinal computation are largely based on stimulation using artificial visual stimuli, and it is unclear how these descriptions map onto the encoding of natural stimuli. We demonstrate that nonlinear spatial integration, a common feature of retinal ganglion cell (RGC) processing, shapes neural responses to natural visual stimuli in primate Off parasol RGCs, whereas On parasol RGCs exhibit surprisingly linear spatial integration. Despite this asymmetry, both cell types show strong nonlinear integration when presented with artificial stimuli. We show that nonlinear integration of natural stimuli is a consequence of rectified excitatory synaptic input and that accounting for nonlinear spatial integration substantially improves models that predict RGC responses to natural images.

Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina

Electrical and chemical synapses coexist in circuits throughout the CNS. Yet, it is not well understood how electrical and chemical synaptic transmission interact to determine the functional output of networks endowed with both types of synapse. We found that release of glutamate from bipolar cells onto retinal ganglion cells (RGCs) was strongly shaped by gap-junction-mediated electrical coupling within the bipolar cell network of the mouse retina. Specifically, electrical synapses spread signals laterally between bipolar cells, and this lateral spread contributed to a nonlinear enhancement of bipolar cell output to visual stimuli presented closely in space and time. Our findings thus (1) highlight how electrical and chemical transmission can work in concert to influence network output and (2) reveal a previously unappreciated circuit mechanism that increases RGC sensitivity to spatiotemporally correlated input, such as that produced by motion.

Chromatic detection from cone photoreceptors to V1 neurons to behavior in rhesus monkeys

Chromatic sensitivity cannot exceed limits set by noise in the cone photoreceptors. To determine how close neurophysiological and psychophysical chromatic sensitivity come to these limits, we developed a parameter-free model of stimulus encoding in the cone outer segments, and we compared the sensitivity of the model to the psychophysical sensitivity of monkeys performing a detection task and to the sensitivity of individual V1 neurons. Modeled cones had a temporal impulse response and a noise power spectrum that were derived from in vitro recordings of macaque cones, and V1 recordings were made during performance of the detection task. The sensitivity of the simulated cone mosaic, the V1 neurons, and the monkeys were tightly yoked for low-spatiotemporal-frequency isoluminant modulations, indicating high-fidelity signal transmission for this class of stimuli. Under the conditions of our experiments and the assumptions for our model, the signal-to-noise ratio for these stimuli dropped by a factor of ∼3 between the cones and perception. Populations of weakly correlated V1 neurons narrowly exceeded the monkeys' chromatic sensitivity but fell well short of the cones' chromatic sensitivity, suggesting that most of the behavior-limiting noise lies between the cone outer segments and the output of V1. The sensitivity gap between the cones and behavior for achromatic stimuli was larger than for chromatic stimuli, indicating greater postreceptoral noise. The cone mosaic model provides a means to compare visual sensitivity across disparate stimuli and to identify sources of noise that limit visual sensitivity.

Distinctive receptive field and physiological properties of a wide-field amacrine cell in the macaque monkey retina

At early stages of visual processing, receptive fields are typically described as subtending local regions of space and thus performing computations on a narrow spatial scale. Nevertheless, stimulation well outside of the classical receptive field can exert clear and significant effects on visual processing. Given the distances over which they occur, the retinal mechanisms responsible for these long-range effects would certainly require signal propagation via active membrane properties. Here the physiology of a wide-field amacrine cell-the wiry cell-in macaque monkey retina is explored, revealing receptive fields that represent a striking departure from the classic structure. A single wiry cell integrates signals over wide regions of retina, 5-10 times larger than the classic receptive fields of most retinal ganglion cells. Wiry cells integrate signals over space much more effectively than predicted from passive signal propagation, and spatial integration is strongly attenuated during blockade of NMDA spikes but integration is insensitive to blockade of NaV channels with TTX. Thus these cells appear well suited for contributing to the long-range interactions of visual signals that characterize many aspects of visual perception.

A simple retinal mechanism contributes to perceptual interactions between rod- and cone-mediated responses in primates

Visual perception across a broad range of light levels is shaped by interactions between rod- and cone-mediated signals. Because responses of retinal ganglion cells, the output cells of the retina, depend on signals from both rod and cone photoreceptors, interactions occurring in retinal circuits provide an opportunity to link the mechanistic operation of parallel pathways and perception. Here we show that rod- and cone-mediated responses interact nonlinearly to control the responses of primate retinal ganglion cells; these nonlinear interactions, surprisingly, were asymmetric, with rod responses strongly suppressing subsequent cone responses but not vice-versa. Human psychophysical experiments revealed a similar perceptual asymmetry. Nonlinear interactions in the retinal output cells were well-predicted by linear summation of kinetically-distinct rod- and cone-mediated signals followed by a synaptic nonlinearity. These experiments thus reveal how a simple mechanism controlling interactions between parallel pathways shapes circuit output and perception.

DOI:http://dx.doi.org/10.7554/eLife.08033.001

The inner surface at the back of the eye is called the retina and contains two types of light-sensitive cells: rod cells and cone cells. Rods outnumber cones by roughly twenty to one and are responsible for vision under low light levels. Cone cells, by contrast, provide detailed vision in bright light, as well as the ability to see in color.

Rods and cones provide input to two distinct networks of cells that convey information in parallel to cells called ganglion cells, which then relay this information out of the retina. However, the signals from activated rods can feed into the cone pathway at several points, meaning that the responses of rods and cones are not independent. At dawn and dusk—and indeed under street lighting at night—rods and cones are both active and interactions between rod and cone responses influence many aspects of vision, including sensitivity to color and contrast.

Grimes et al. have now identified a neural mechanism behind these interactions by combining measurements of human vision with recordings of electrical activity in retinas from non-human primates. The experiments confirmed that activating either type of photoreceptor briefly suppresses the responses of the other, although unexpectedly rods inhibit cones more than cones inhibit rods. The site of this interaction is the connection—or synapse—between the very last cell in the cone pathway and the retinal output cells. Prior to this ‘gateway’ synapse, rod and cone-mediated responses are largely independent.

Vision at dawn and dusk is shaped by a complex set of interactions between rod and cone signals—such as the ability of activated rods to change color perception at dusk. These findings show that these seemingly complex behaviors can arise from simple interactions at the level of neural circuits.

DOI:http://dx.doi.org/10.7554/eLife.08033.002

Complex inhibitory microcircuitry regulates retinal signaling near visual threshold

Neuronal microcircuits, small, localized signaling motifs involving two or more neurons, underlie signal processing and computation in the brain. Compartmentalized signaling within a neuron may enable it to participate in multiple, independent microcircuits. Each A17 amacrine cell in the mammalian retina contains within its dendrites hundreds of synaptic feedback microcircuits that operate independently to modulate feedforward signaling in the inner retina. Each of these microcircuits comprises a small (<1 μm) synaptic varicosity that typically receives one excitatory synapse from a presynaptic rod bipolar cell (RBC) and returns two reciprocal inhibitory synapses back onto the same RBC terminal. Feedback inhibition from the A17 sculpts the feedforward signal from the RBC to the AII, a critical component of the circuitry mediating night vision. Here, we show that the two inhibitory synapses from the A17 to the RBC express kinetically distinct populations of GABA receptors: rapidly activating GABA(A)Rs are enriched at one synapse while more slowly activating GABA(C)Rs are enriched at the other. Anatomical and electrophysiological data suggest that macromolecular complexes of voltage-gated (Cav) channels and Ca(2+)-activated K(+) channels help to regulate GABA release from A17 varicosities and limit GABA(C)R activation under certain conditions. Finally, we find that selective elimination of A17-mediated feedback inhibition reduces the signal to noise ratio of responses to dim flashes recorded in the feedforward pathway (i.e., the AII amacrine cell). We conclude that A17-mediated feedback inhibition improves the signal to noise ratio of RBC-AII transmission near visual threshold, thereby improving visual sensitivity at night.