scholarly journals Testing pseudo-linear models of responses to natural scenes in primate retina

2016 ◽  
Author(s):  
Alexander Heitman ◽  
Nora Brackbill ◽  
Martin Greschner ◽  
Alexander Sher ◽  
Alan M. Litke ◽  
...  
Keyword(s):  
Linear Model ◽  
Visual Processing ◽  
Large Scale ◽  
Linear Models ◽  
Ganglion Cells ◽  
Visual Stimuli ◽  
Natural Scenes ◽  
Natural Environments ◽  
Linear Filtering ◽  
Fixational Eye Movements

A central goal of systems neuroscience is to develop accurate quantitative models of how neural circuits process information. Prevalent models of light response in retinal ganglion cells (RGCs) usually begin with linear filtering over space and time, which reduces the high-dimensional visual stimulus to a simpler and more tractable scalar function of time that in turn determines the model output. Although these pseudo-linear models can accurately replicate RGC responses to stochastic stimuli, it is unclear whether the strong linearity assumption captures the function of the retina in the natural environment. This paper tests how accurately one pseudo-linear model, the generalized linear model (GLM), explains the responses of primate RGCs to naturalistic visual stimuli. Light responses from macaque RGCs were obtained using large-scale multi-electrode recordings, and two major cell types, ON and OFF parasol, were examined. Visual stimuli consisted of images of natural environments with simulated saccadic and fixational eye movements. The GLM accurately reproduced RGC responses to white noise stimuli, as observed previously, but did not generalize to predict RGC responses to naturalistic stimuli. It also failed to capture RGC responses when fitted and tested with naturalistic stimuli alone. Fitted scalar nonlinearities before and after the linear filtering stage were insufficient to correct the failures. These findings suggest that retinal signaling under natural conditions cannot be captured by models that begin with linear filtering, and emphasize the importance of additional spatial nonlinearities, gain control, and/or peripheral effects in the first stage of visual processing.

scholarly journals The dynamic neural code of the retina for natural scenes

2018 ◽  
Author(s):  
Niru Maheswaranathan ◽  
Lane T. McIntosh ◽  
Hidenori Tanaka ◽  
Satchel Grant ◽  
David B. Kastner ◽  
...  
Keyword(s):  
Visual Processing ◽  
Ganglion Cells ◽  
Predictive Coding ◽  
Neural Code ◽  
Natural Scenes ◽  
Large Set ◽  
Fundamental Limits ◽  
Fundamental Goal ◽  
Latent Effects ◽  
Highly Correlated

AbstractUnderstanding how the visual system encodes natural scenes is a fundamental goal of sensory neuroscience. We show here that a three-layer network model predicts the retinal response to natural scenes with an accuracy nearing the fundamental limits of predictability. The model’s internal structure is interpretable, in that model units are highly correlated with interneurons recorded separately and not used to fit the model. We further show the ethological relevance to natural visual processing of a diverse set of phenomena of complex motion encoding, adaptation and predictive coding. Our analysis uncovers a fast timescale of visual processing that is inaccessible directly from experimental data, showing unexpectedly that ganglion cells signal in distinct modes by rapidly (< 0.1 s) switching their selectivity for direction of motion, orientation, location and the sign of intensity. A new approach that decomposes ganglion cell responses into the contribution of interneurons reveals how the latent effects of parallel retinal circuits generate the response to any possible stimulus. These results reveal extremely flexible and rapid dynamics of the retinal code for natural visual stimuli, explaining the need for a large set of interneuron pathways to generate the dynamic neural code for natural scenes.

scholarly journals Hybrid MARMA-NARX model for flow forecasting based on the large-scale climate signals, sea-surface temperatures, and rainfall

2018 ◽  
Vol 49 (6) ◽  
pp. 1788-1803 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Arezoo Ahmadian ◽  
Mohammad Valipour
Keyword(s):  
Linear Model ◽  
Hybrid Model ◽  
Large Scale ◽  
Linear Models ◽  
Moving Average ◽  
Effective Parameters ◽  
Forecasting Accuracy ◽  
Local Factor ◽  
Non Linear ◽  
Climate Signals

Abstract In this study, to reflect the effect of large-scale climate signals on runoff, these indices are accompanied with rainfall (the most effective local factor in runoff) as the inputs of the hybrid model. Where one-year in advance forecasting of reservoir inflows can provide data to have an optimal reservoir operation, reports show we still need more accurate models which include all effective parameters to have more forecasting accuracy than traditional linear models (ARMA and ARIMA). Thus, hybridization of models was employed for improving the accuracy of flow forecasting. Moreover, various forecasters including large-scale climate signals were tested to promote forecasting. This paper focuses on testing MARMA-NARX hybrid model to enhance the accuracy of monthly inflow forecasts. Since the inflow in different periods of the year has in linear and non-linear trends, the hybrid model is proposed as a means of combining linear model, monthly autoregressive moving average (MARMA), and non-linear model, nonlinear autoregressive model with exogenous (NARX) inputs to upgrade the accuracy of flow forecasting. The results of the study showed enhanced forecasting accuracy through using the hybrid model.

scholarly journals Heterogeneous Response Dynamics in Retinal Ganglion Cells: The Interplay of Predictive Coding and Adaptation

2010 ◽  
Vol 103 (6) ◽  
pp. 3184-3194 ◽  
Author(s):  
Sheila Nirenberg ◽  
Illya Bomash ◽  
Jonathan W. Pillow ◽  
Jonathan D. Victor
Keyword(s):  
White Noise ◽  
Visual Processing ◽  
Ganglion Cells ◽  
Predictive Coding ◽  
Sensory Systems ◽  
Natural Environments ◽  
Retinal Ganglion ◽  
Response Dynamics ◽  
Low Frequencies ◽  
Environmental Correlations

To make efficient use of their limited signaling capacity, sensory systems often use predictive coding. Predictive coding works by exploiting the statistical regularities of the environment—specifically, by filtering the sensory input to remove its predictable elements, thus enabling the neural signal to focus on what cannot be guessed. To do this, the neural filters must remove the environmental correlations. If predictive coding is to work well in multiple environments, sensory systems must adapt their filtering properties to fit each environment's statistics. Using the visual system as a model, we determine whether this happens. We compare retinal ganglion cell dynamics in two very different environments: white noise and natural. Because natural environments have more power than that of white noise at low temporal frequencies, predictive coding is expected to produce a suppression of low frequencies and an enhancement of high frequencies, compared with the behavior in a white-noise environment. We find that this holds, but only in part. First, predictive coding behavior is not uniform: most on cells manifest it, whereas off cells, on average, do not. Overlaid on this nonuniformity between cell classes is further nonuniformity within both cell classes. These findings indicate that functional considerations beyond predictive coding play an important role in shaping the dynamics of sensory adaptation. Moreover, the differences in behavior between on and off cell classes add to the growing evidence that these classes are not merely homogeneous mirror images of each other and suggest that their roles in visual processing are more complex than expected from the classic view.

A model of the dynamics of retinal activity during natural visual fixation

2007 ◽  
Vol 24 (2) ◽  
pp. 217-230 ◽  
Author(s):  
GAËLLE DESBORDES ◽  
MICHELE RUCCI
Keyword(s):  
Eye Movements ◽  
Visual Field ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Visual Fixation ◽  
Natural Scenes ◽  
M Cells ◽  
Retinal Activity ◽  
Cell Responses ◽  
Fixational Eye Movements

During visual fixation, small eye movements keep the retinal image continuously in motion. It is known that neurons in the visual system are sensitive to the spatiotemporal modulations of luminance resulting from this motion. In this study, we examined the influence of fixational eye movements on the statistics of neural activity in the macaque's retina during the brief intersaccadic periods of natural visual fixation. The responses of parvocellular (P) and magnocellular (M) ganglion cells in different regions of the visual field were modeled while their receptive fields scanned natural images following recorded traces of eye movements. Immediately after the onset of fixation, wide ensembles of coactive ganglion cells extended over several degrees of visual angle, both in the central and peripheral regions of the visual field. Following this initial pattern of activity, the covariance between the responses of pairs of P and M cells and the correlation between the responses of pairs of M cells dropped drastically during the course of fixation. Cell responses were completely uncorrelated by the end of a typical 300-ms fixation. This dynamic spatial decorrelation of retinal activity is a robust phenomenon independent of the specifics of the model. We show that it originates from the interaction of three factors: the statistics of natural scenes, the small amplitudes of fixational eye movements, and the temporal sensitivities of ganglion cells. These results support the hypothesis that fixational eye movements, by shaping the statistics of retinal activity, are an integral component of early visual representations.

scholarly journals Efficient coding of natural scenes improves neural system identification

2022 ◽  
Author(s):  
Yongrong Qiu ◽  
David A Klindt ◽  
Klaudia P Szatko ◽  
Dominic Gonschorek ◽  
Larissa Hoefling ◽  
...  
Keyword(s):  
System Identification ◽  
Visual Processing ◽  
Neural System ◽  
Natural Scenes ◽  
Natural Environments ◽  
Retinal Neurons ◽  
Efficient Coding ◽  
Recorded Data ◽  
Identification Model ◽  
Regularized Model

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

Estimating large-scale structures in wall turbulence using linear models

2018 ◽  
Vol 842 ◽  
pp. 146-162 ◽  
Author(s):  
Simon J. Illingworth ◽  
Jason P. Monty ◽  
Ivan Marusic
Keyword(s):  
Linear Model ◽  
Large Scale ◽  
Eddy Viscosity ◽  
Linear Models ◽  
Systems Approach ◽  
Wall Turbulence ◽  
Linear Estimator ◽  
Small Scale ◽  
Spanwise Direction ◽  
Time Resolved

A dynamical systems approach is used to devise a linear estimation tool for channel flow at a friction Reynolds number of $Re_{\unicode[STIX]{x1D70F}}=1000$. The estimator uses time-resolved velocity measurements at a single wall-normal location to estimate the velocity field at other wall-normal locations (the data coming from direct numerical simulations). The estimation tool builds on the work of McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382) by using a Navier–Stokes-based linear model and treating any nonlinear terms as unknown forcings to an otherwise linear system. In this way nonlinearities are not ignored, but instead treated as an unknown model input. It is shown that, while the linear estimator qualitatively reproduces large-scale flow features, it tends to overpredict the amplitude of velocity fluctuations – particularly for structures that are long in the streamwise direction and thin in the spanwise direction. An alternative linear model is therefore formed in which a simple eddy viscosity is used to model the influence of the small-scale turbulent fluctuations on the large scales of interest. This modification improves the estimator performance significantly. Importantly, as well as improving the performance of the estimator, the linear model with eddy viscosity is also able to predict with reasonable accuracy the range of wavenumber pairs and the range of wall-normal heights over which the estimator will perform well.

A Computational Framework for Realistic Retina Modeling

2016 ◽  
Vol 26 (07) ◽  
pp. 1650030 ◽  
Author(s):  
Pablo Martínez-Cañada ◽  
Christian Morillas ◽  
Begoña Pino ◽  
Eduardo Ros ◽  
Francisco Pelayo
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Ganglion Cells ◽  
Building Blocks ◽  
Motion Sensitivity ◽  
Differential Motion ◽  
Electrophysiological Recordings ◽  
Processing Pathways ◽  
Scale Levels ◽  
Higher Visual Areas

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

scholarly journals Decorrelation of retinal response to natural scenes by fixational eye movements

2015 ◽  
Vol 112 (10) ◽  
pp. 3110-3115 ◽  
Author(s):  
Irina Yonit Segal ◽  
Chen Giladi ◽  
Michael Gedalin ◽  
Michele Rucci ◽  
Mor Ben-Tov ◽  
...  
Keyword(s):  
Eye Movements ◽  
Retinal Ganglion Cells ◽  
Visual Information ◽  
Ganglion Cells ◽  
Natural World ◽  
Natural Scenes ◽  
Retinal Ganglion ◽  
Spatial Correlations ◽  
Fixational Eye Movements ◽  
Spatiotemporal Signal

Under natural viewing conditions the input to the retina is a complex spatiotemporal signal that depends on both the scene and the way the observer moves. It is commonly assumed that the retina processes this input signal efficiently by taking into account the statistics of the natural world. It has recently been argued that incessant microscopic eye movements contribute to this process by decorrelating the input to the retina. Here we tested this theory by measuring the responses of the salamander retina to stimuli replicating the natural input signals experienced by the retina in the presence and absence of fixational eye movements. Contrary to the predictions of classic theories of efficient encoding that do not take behavior into account, we show that the response characteristics of retinal ganglion cells are not sufficient in themselves to disrupt the broad correlations of natural scenes. Specifically, retinal ganglion cells exhibited strong and extensive spatial correlations in the absence of fixational eye movements. However, the levels of correlation in the neural responses dropped in the presence of fixational eye movements, resulting in effective decorrelation of the channels streaming information to the brain. These observations confirm the predictions that microscopic eye movements act to reduce correlations in retinal responses and contribute to visual information processing.

scholarly journals Pathway-specific asymmetries between ON and OFF visual signals

2018 ◽  
Author(s):  
Sneha Ravi ◽  
Daniel Ahn ◽  
Martin Greschner ◽  
E.J Chichilnisky ◽  
Greg D. Field
Keyword(s):  
Visual Processing ◽  
Functional Organization ◽  
Ganglion Cells ◽  
Temporal Integration ◽  
Receptive Fields ◽  
Visual Signals ◽  
Natural Scenes ◽  
Alpha Cells ◽  
Spatial Integration ◽  
On And Off Pathways

AbstractVisual processing is largely organized into ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways exhibit natural pairings based on morphological and physiological similarities, such as ON and OFF alpha ganglion cells in the mammalian retina. Several studies have noted asymmetries in the properties of ON and OFF pathways. For example, the spatial receptive fields (RFs) of OFF alpha cells are systematically smaller than ON alpha cells. Analysis of natural scenes suggests these asymmetries are optimal for visual encoding. To test the generality of ON-OFF asymmetries, we measured the spatiotemporal RF properties of multiple RGC types in rat retina. Through a quantitative and serial classification, we identified three functional pairs of ON and OFF RGCs. We analyzed the structure of their RFs and compared spatial integration, temporal integration, and gain across ON and OFF pairs. Similar to previous results from cat and primate, RGC types with larger spatial RFs exhibited briefer temporal integration and higher gain. However, each pair of ON and OFF RGC types exhibited distinct asymmetric relationships between receptive field properties, some of which were opposite to previous reports. These results reveal the functional organization of six RGC types in the rodent retina and indicate that ON-OFF asymmetries are pathway specific.Significance StatementCircuits that process sensory input frequently process increments separately from decrements, so called ‘ON’ and ‘OFF’ responses. Theoretical studies indicate this separation, and associated asymmetries in ON and OFF pathways, may be beneficial for encoding natural stimuli. However, the generality of ON and OFF pathway asymmetries has not been tested. Here we compare the functional properties of three distinct pairs of ON and OFF pathways in the rodent retina and show their asymmetries are pathway specific. These results provide a new view on the partitioning of vision across diverse ON and OFF signaling pathways

scholarly journals The emergence of multiple retinal cell types through efficient coding of natural movies

2018 ◽  
Author(s):  
Samuel A. Ocko ◽  
Jack Lindsey ◽  
Surya Ganguli ◽  
Stephane Deny
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Cell Types ◽  
Retinal Cell ◽  
Retinal Eccentricity ◽  
Natural Scenes ◽  
Efficient Coding ◽  
Cell Densities

AbstractOne of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells.

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