scholarly journals The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

2017 ◽  
Vol 118 (6) ◽  
pp. 3051-3091 ◽  
Author(s):  
Tadamasa Sawada ◽  
Alexander A. Petrov
Keyword(s):  
Physiological Data ◽  
Simulation Experiments ◽  
Complex Cells ◽  
V1 Neurons ◽  
Wide Range ◽  
Divisive Normalization ◽  
Standard Parameter ◽  
Comprehensive Comparison ◽  
Simple And Complex Cells ◽  
Mathematical Analyses

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181–197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

scholarly journals The Divisive-Normalization Model of V1 Neurons: A Comprehensive Comparison of Physiological Data and Model Predictions

2016 ◽  
Author(s):  
Tadamasa Sawada ◽  
Alexander A. Petrov
Keyword(s):  
Physiological Data ◽  
Simulation Experiments ◽  
Complex Cells ◽  
V1 Neurons ◽  
Wide Range ◽  
Divisive Normalization ◽  
Standard Parameter ◽  
Comprehensive Comparison ◽  
Simple And Complex Cells ◽  
Mathematical Analyses

AbstractThe physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM, Heeger, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing-rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

Cone Inputs to Simple and Complex Cells in V1 of Awake Macaque

2007 ◽  
Vol 97 (4) ◽  
pp. 3070-3081 ◽  
Author(s):  
Gregory D. Horwitz ◽  
E. J. Chichilnisky ◽  
Thomas D. Albright
Keyword(s):  
Linear Models ◽  
Noise Analysis ◽  
Receptive Fields ◽  
Light Responses ◽  
Simple Cells ◽  
Complex Cells ◽  
V1 Neurons ◽  
Spike Triggered Averaging ◽  
Simple And Complex Cells ◽  
Simple And Complex Cell

Rules by which V1 neurons combine signals originating in the cone photoreceptors are poorly understood. We measured cone inputs to V1 neurons in awake, fixating monkeys with white-noise analysis techniques that reveal properties of light responses not revealed by purely linear models used in previous studies. Simple cells were studied by spike-triggered averaging that is robust to static nonlinearities in spike generation. This analysis revealed, among heterogeneously tuned neurons, two relatively discrete categories: one with opponent L- and M-cone weights and another with nonopponent cone weights. Complex cells were studied by spike-triggered covariance, which identifies features in the stimulus sequence that trigger spikes in neurons with receptive fields containing multiple linear subunits that combine nonlinearly. All complex cells responded to nonopponent stimulus modulations. Although some complex cells responded to cone-opponent stimulus modulations too, none exhibited the pure opponent sensitivity observed in many simple cells. These results extend the findings on distinctions between simple and complex cell chromatic tuning observed in previous studies in anesthetized monkeys.

Modeling V1 Disparity Tuning to Time-Varying Stimuli

2001 ◽  
Vol 86 (1) ◽  
pp. 143-155 ◽  
Author(s):  
Yuzhi Chen ◽  
Yunjiu Wang ◽  
Ning Qian
Keyword(s):  
Large Body ◽  
Cell Types ◽  
Physiological Data ◽  
Time Varying ◽  
Temporal Response ◽  
Temporal Dimension ◽  
Complex Cells ◽  
Motion In Depth ◽  
Random Dot Stereograms ◽  
Simple And Complex Cells

Most models of disparity selectivity consider only the spatial properties of binocular cells. However, the temporal response is an integral component of real neurons' activities, and time-varying stimuli are often used in the experiments of disparity tuning. To understand the temporal dimension of V1 disparity representation, we incorporate a specific temporal response function into the disparity energy model and demonstrate that the binocular interaction of complex cells is separable into a Gabor disparity function and a positive time function. We then investigate how the model simple and complex cells respond to widely used time-varying stimuli, including motion-in-depth patterns, drifting gratings, moving bars, moving random-dot stereograms, and dynamic random-dot stereograms. It is found that both model simple and complex cells show more reliable disparity tuning to time-varying stimuli than to static stimuli, but similarities in the disparity tuning between simple and complex cells depend on the stimulus. Specifically, the disparity tuning curves of the two cell types are similar to each other for either drifting sinusoidal gratings or moving bars. In contrast, when the stimuli are dynamic random-dot stereograms, the disparity tuning of simple cells is highly variable, whereas the tuning of complex cells remains reliable. Moreover, cells with similar motion preferences in the two eyes cannot be truly tuned to motion in depth regardless of the stimulus types. These simulation results are consistent with a large body of extant physiological data, and provide some specific, testable predictions.

scholarly journals Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics

2006 ◽  
Vol 18 (11) ◽  
pp. 2680-2718 ◽  
Author(s):  
Odelia Schwartz ◽  
Terrence J. Sejnowski ◽  
Peter Dayan
Keyword(s):  
Generative Model ◽  
Natural Images ◽  
Statistical Characteristics ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Scale Mixture ◽  
Complex Cells ◽  
Gaussian Scale Mixture ◽  
Wide Range ◽  
Divisive Normalization

Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixturemodel that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.

scholarly journals Constraint-based metabolic control analysis for rational strain engineering

2020 ◽  
Author(s):  
Sophia Tsouka ◽  
Meric Ataman ◽  
Tuure Hameri ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Metabolic Engineering ◽  
Genome Editing ◽  
Metabolic Control ◽  
Metabolic Flux ◽  
Strain Engineering ◽  
Metabolic Control Analysis ◽  
Physiological Data ◽  
Response Analysis ◽  
Control Analysis ◽  
Wide Range

AbstractThe advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

Thermal Protection System for Underwater Use in Cold and Hot Water

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Joseph C. Mollendorf ◽  
David R. Pendergast
Keyword(s):  
Thermal Protection ◽  
Cold Water ◽  
Electrical Power ◽  
Thermal Protection System ◽  
Hot Water ◽  
Protection System ◽  
Working Fluid ◽  
Physiological Data ◽  
Wide Range ◽  
Body Core

Underwater workers, sport and military divers, are exposed to thermal stress since most of the waters of the world are below or above what is thermally neutral. Although divers wear insulation suits for passive thermal protection they are inadequate. Active heating is currently accomplished by resistive heating and open-flow tubes delivering hot water; however, these methods are problematic. The challenge of this project was to design, build and test an active diver thermal protection system (DTPS) to be used with wet suit insulation that is effective, user-friendly, reliable, and that could be used by a free-swimming diver. The DTPS has a minimum number of moving parts, is low maintenance, has no unsafe or toxic working fluid and uses no consumables except a safe, high density, modular electrical power source. A portable and swimmable, self-contained, electrically powered unit (DTPS) has been designed, built, and tested that produces and circulates thermally conditioned water in a closed-loop through a zoned tube suit worn by a diver under a wetsuit to maintain skin and body core temperatures within prescribed safe limits. The system has been validated by using physiological data taken on human subjects over a wide range of ambient water temperatures. Corresponding enthalpy and electrical power measurements were used as the basis of a thermodynamic analysis. The DTPS maintained skin and body core temperatures within safe and functional ranges by providing up to about 200 W of heating in cold water and up to about 330 W of cooling in hot water. The corresponding electrical power consumption was up to about 300 W in cold water and up to about 1500 W in hot water. The results of a complete audit of the power use and heat transfer are presented along with the efficiency of the thermoelectric heating/cooling modules and the duty cycle of the system for a range of water immersion temperatures from 10°C to 39°C. The DTPS proved to be an effective and reliable apparatus for diver thermal protection in water temperatures from 10°C to 39°C, which covers most of the range of the earth’s waters. The data presented here can be used to modify the design of the DTPS to meet specific needs of the diving community.

Selective effects of aging on simple and complex cells in primary visual cortex of rhesus monkeys

2012 ◽  
Vol 1470 ◽  
pp. 17-23 ◽  
Author(s):  
Zhen Liang ◽  
Hongxin Li ◽  
Yun Yang ◽  
Guangxing Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Rhesus Monkeys ◽  
Complex Cells ◽  
Simple And Complex Cells ◽  
Effects Of Aging ◽  
Selective Effects

Secure Framework for Internet of Things Based e-Health System

2019 ◽  
Vol 10 (4) ◽  
pp. 16-29 ◽  
Author(s):  
Adil Bashir ◽  
Ajaz Hussain Mir
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
Physiological Data ◽  
Electronic Patient Records ◽  
Security Framework ◽  
Security Issues ◽  
Wide Range ◽  
Promising Application ◽  
In Transit ◽  
Secure Transmission

Internet of Things (IoT) is the emerging technology finding applications in a wide range of fields that include smart homes, intelligent transportation, e-health, supply chain management. Among IoT applications, e-health is one of the most promising application in which smart devices capable of monitoring physiological parameters of patients are implanted in or around their bodies which automatically sense and transmit collected data to medical consultants. However, security issues for electronic patient records (EPR) in-transit hinder the usage of IoT in e-health systems. Among these issues, EPR confidentiality and entity authentication are major concerns. In this article, confidentiality of EPR and its secure transmission over network is focused mainly. A security framework is proposed where-in smart devices encrypt sensed physiological data with Light-Weight Encryption Algorithm and Advanced Encryption Standard cryptographic algorithms. The security framework and the designed protocol provides better security and are energy efficient as presented in the evaluation section.

scholarly journals Spatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17

2010 ◽  
Vol 103 (2) ◽  
pp. 677-697 ◽  
Author(s):  
Lionel G. Nowak ◽  
Maria V. Sanchez-Vives ◽  
David A. McCormick
Keyword(s):  
Spatial Frequency ◽  
Response Latency ◽  
Frequency Tuning ◽  
Receptive Fields ◽  
Width Ratio ◽  
Area 17 ◽  
Simple Cells ◽  
Complex Cells ◽  
Temporal Features ◽  
Simple And Complex Cells

The aim of the present study was to characterize the spatial and temporal features of synaptic and discharge receptive fields (RFs), and to quantify their relationships, in cat area 17. For this purpose, neurons were recorded intracellularly while high-frequency flashing bars were used to generate RFs maps for synaptic and spiking responses. Comparison of the maps shows that some features of the discharge RFs depended strongly on those of the synaptic RFs, whereas others were less dependent. Spiking RF duration depended poorly and spiking RF amplitude depended moderately on those of the underlying synaptic RFs. At the other extreme, the optimal spatial frequency and phase of the discharge RFs in simple cells were almost entirely inherited from those of the synaptic RFs. Subfield width, in both simple and complex cells, was less for spiking responses compared with synaptic responses, but synaptic to discharge width ratio was relatively variable from cell to cell. When considering the whole RF of simple cells, additional variability in width ratio resulted from the presence of additional synaptic subfields that remained subthreshold. Due to these additional, subthreshold subfields, spatial frequency tuning predicted from synaptic RFs appears sharper than that predicted from spiking RFs. Excitatory subfield overlap in spiking RFs was well predicted by subfield overlap at the synaptic level. When examined in different regions of the RF, latencies appeared to be quite variable, but this variability showed negligible dependence on distance from the RF center. Nevertheless, spiking response latency faithfully reflected synaptic response latency.

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