scholarly journals Unraveling the mechanisms of surround suppression in early visual processing

2021 ◽  
Vol 17 (4) ◽  
pp. e1008916
Author(s):  
Yao Li ◽  
Lai-Sang Young
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Neural Circuit ◽  
Surround Suppression ◽  
Unknown Parameters ◽  
Tuning Curves ◽  
Recurrent Excitation ◽  
Inhibitory Feedback ◽  
Layer 4 ◽  
I Cells

This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit alistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also discussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parameters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the introduction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems.

scholarly journals Quantitative synapse analysis for cell-type specific connectomics

2018 ◽  
Author(s):  
Dika A. Kuljis ◽  
Khaled Zemoura ◽  
Cheryl A. Telmer ◽  
Jiseok Lee ◽  
Eunsol Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Neural Circuit ◽  
Apical Dendrite ◽  
Automated Detection ◽  
Cell Type ◽  
Disease States ◽  
Specific Localization ◽  
Cell Type Specific ◽  
Dendritic Branches ◽  
Circuit Function

AbstractAnatomical methods for determining cell-type specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed new genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)-or YFP-coupled, postsynaptically-localized neuroligin-1 targeting sequence (FAP/YFPpost). Sparse viral expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific localization of synapses across diverse neuron types in mouse somatosensory cortex. High-resolution confocal image stacks of virally-transduced neurons were used for 3D reconstructions of postsynaptic cells and automated detection of synaptic puncta. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, synapses, and presynaptic neurites to assess subtype-specific inhibitory connectivity onto L2 neocortical pyramidal (Pyr) neurons. Quantitative and compartment-specific comparisons show that PV inputs are the dominant source of inhibition at both the soma and across all dendritic branches examined and were particularly concentrated at the primary apical dendrite, a previously unrecognized compartment of L2 Pyr neurons. Our fluorescence-based synapse labeling reagents will facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states.

scholarly journals Organization of the Drosophila larval visual circuit

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ivan Larderet ◽  
Pauline MJ Fritsch ◽  
Nanae Gendre ◽  
G Larisa Neagu-Maier ◽  
Richard D Fetter ◽  
...  
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Neural Circuit ◽  
Visual Features ◽  
Environmental Cues ◽  
Wiring Diagram ◽  
Visual Systems ◽  
Drosophila Larvae ◽  
Organizational Principles ◽  
Numerical Complexity

Visual systems transduce, process and transmit light-dependent environmental cues. Computation of visual features depends on photoreceptor neuron types (PR) present, organization of the eye and wiring of the underlying neural circuit. Here, we describe the circuit architecture of the visual system of Drosophila larvae by mapping the synaptic wiring diagram and neurotransmitters. By contacting different targets, the two larval PR-subtypes create two converging pathways potentially underlying the computation of ambient light intensity and temporal light changes already within this first visual processing center. Locally processed visual information then signals via dedicated projection interneurons to higher brain areas including the lateral horn and mushroom body. The stratified structure of the larval optic neuropil (LON) suggests common organizational principles with the adult fly and vertebrate visual systems. The complete synaptic wiring diagram of the LON paves the way to understanding how circuits with reduced numerical complexity control wide ranges of behaviors.

A Logit Model for Budget Allocation Subject to Multi Budget Sources

2011 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Saeed A. Bagloee ◽  
Christopher G. Reddick
Keyword(s):  
Logit Model ◽  
Large Scale ◽  
Budget Allocation ◽  
Solution Algorithm ◽  
Influential Factors ◽  
Programming Formulation ◽  
Extended System ◽  
Multiple Sources ◽  
Unknown Parameters ◽  
Allocation Process

In a complex and extended system such as a government, the proper allocation of the budget to its sub-entities is always a major challenge. As such for cases like governments, a situation in which multiple budget sources with different concerns available to the sub-entities is common. This study develops an applicable model for large-scale cases in which identifying the flow of capital or budget from (multiple) sources to the sub-entities is sought. Since the influential factors to the allocation process may be mingled with some unknown parameters (as well as known factors) a logit model is developed from past panel data. The logit model is based on the concept of utility, which quantifies the advantage of approaching budget-sources for the sub-entities. Then the budget allocation problem of logit form is written as a mathematical programming formulation for which Successive Coordinate Descent (SCD) method is proposed as the solution algorithm. In this paper, the proposed methodology is tested numerically. The results of this study show there is strong evidence that some of the entities’ properties can be altered in order to achieve a better budget allocation.

scholarly journals Asymmetric Temporal Integration of Layer 4 and Layer 2/3 Inputs in Visual Cortex

2011 ◽  
Vol 105 (1) ◽  
pp. 347-355 ◽  
Author(s):  
Giao B. Hang ◽  
Yang Dan
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Pyramidal Neurons ◽  
Temporal Integration ◽  
Cortical Inhibition ◽  
Multiple Sources ◽  
Layer 2 ◽  
Layer 4 ◽  
The Difference

Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices. We found that the integration is sublinear and temporally asymmetric, with larger responses if layer 2/3 input preceded layer 4 input. The sublinearity depended on inhibition, and the asymmetry was largely attributable to the difference between the two inhibitory inputs. Interestingly, the asymmetric integration was specific to pyramidal neurons, and it strongly affected their spiking output. Thus via cortical inhibition, the temporal order of activation of layer 2/3 and layer 4 pathways can exert powerful control of cortical output during visual processing.

Orientation Encoding and Viewpoint Invariance in Face Recognition: Inferring Neural Properties from Large-Scale Signals

2018 ◽  
Vol 24 (6) ◽  
pp. 582-608 ◽  
Author(s):  
Fernando M. Ramírez
Keyword(s):  
Face Recognition ◽  
Visual Processing ◽  
Large Scale ◽  
Bilateral Symmetry ◽  
Distributed Network ◽  
Face Area ◽  
Orientation Information ◽  
Orientation Perception ◽  
Multivariate Pattern

Viewpoint-invariant face recognition is thought to be subserved by a distributed network of occipitotemporal face-selective areas that, except for the human anterior temporal lobe, have been shown to also contain face-orientation information. This review begins by highlighting the importance of bilateral symmetry for viewpoint-invariant recognition and face-orientation perception. Then, monkey electrophysiological evidence is surveyed describing key tuning properties of face-selective neurons—including neurons bimodally tuned to mirror-symmetric face-views—followed by studies combining functional magnetic resonance imaging (fMRI) and multivariate pattern analyses to probe the representation of face-orientation and identity information in humans. Altogether, neuroimaging studies suggest that face-identity is gradually disentangled from face-orientation information along the ventral visual processing stream. The evidence seems to diverge, however, regarding the prevalent form of tuning of neural populations in human face-selective areas. In this context, caveats possibly leading to erroneous inferences regarding mirror-symmetric coding are exposed, including the need to distinguish angular from Euclidean distances when interpreting multivariate pattern analyses. On this basis, this review argues that evidence from the fusiform face area is best explained by a view-sensitive code reflecting head angular disparity, consistent with a role of this area in face-orientation perception. Finally, the importance is stressed of explicit models relating neural properties to large-scale signals.

A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. F57-F71 ◽  
Author(s):  
Philipp A. Witte ◽  
Mathias Louboutin ◽  
Navjot Kukreja ◽  
Fabio Luporini ◽  
Michael Lange ◽  
...  
Keyword(s):  
User Interfaces ◽  
Code Generation ◽  
Large Scale ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
Linear Operators ◽  
Automatic Code Generation ◽  
Unknown Parameters ◽  
And Performance ◽  
Automatic Code

Writing software packages for seismic inversion is a very challenging task because problems such as full-waveform inversion or least-squares imaging are algorithmically and computationally demanding due to the large number of unknown parameters and the fact that waves are propagated over many wavelengths. Therefore, software frameworks need to combine versatility and performance to provide geophysicists with the means and flexibility to implement complex algorithms that scale to exceedingly large 3D problems. Following these principles, we have developed the Julia Devito Inversion framework, an open-source software package in Julia for large-scale seismic modeling and inversion based on Devito, a domain-specific language compiler for automatic code generation. The framework consists of matrix-free linear operators for implementing seismic inversion algorithms that closely resemble the mathematical notation, a flexible resilient parallelization, and an interface to Devito for generating optimized stencil code to solve the underlying wave equations. In comparison with many manually optimized industry codes written in low-level languages, our software is built on the idea of independent layers of abstractions and user interfaces with symbolic operators. Through a series of numerical examples, we determined that this allows users to implement a series of increasingly complex algorithms for waveform inversion and imaging as simple Julia scripts that scale to large-scale 3D problems. This illustrates that software based on the paradigms of abstract user interfaces and automatic code generation and makes it possible to manage the complexity of the algorithms and performance optimizations, thus providing a high-performance research and production framework.

scholarly journals Visual processing mode switching regulated by VIP cells

2016 ◽  
Author(s):  
Jung Hoon Lee ◽  
Stefan Mihalas
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Moving Objects ◽  
Pyramidal Neurons ◽  
Mode Switching ◽  
Rate Model ◽  
Surround Suppression ◽  
Visual Responses ◽  
Processing Mode ◽  
Cell Depolarization

AbstractThe responses of neurons in mouse primary visual cortex (V1) to visual stimuli depend on behavioral states. Specifically, surround suppression is reduced during locomotion. Although locomotion-induced vasoactive intestinal polypeptide positive (VIP) interneuron depolarization can account for the reduction of surround suppression, the functions of VIP cell depolarization are not fully understood. Here we utilize a firing rate model and a computational model to elucidate the potential functions of VIP cell depolarization during locomotion. Our analyses suggest 1) that surround suppression sharpens the visual responses in V1 to a stationary scene, 2) that depolarized VIP cells enhance V1 responses to moving objects by reducing self-induced surround suppression and 3) that during locomotion V1 neuron responses to some features of the moving objects can be selectively enhanced. Thus, VIP cells regulate surround suppression to allow pyramidal neurons to optimally encode visual information independent of behavioral state.

scholarly journals State-dependent control of cortical processing speed via gain modulation

2020 ◽  
Author(s):  
David Wyrick ◽  
Luca Mazzucato
Keyword(s):  
Information Processing ◽  
Processing Speed ◽  
Visual Processing ◽  
Large Scale ◽  
Internal State ◽  
Gain Modulation ◽  
Cell Type ◽  
Information Processing Speed ◽  
Afferent Projections ◽  
Cell Type Specific

AbstractTo thrive in dynamic environments, animals can generate flexible behavior and rapidly adapt responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual modulations may occur early in the cortical hierarchy and may be implemented via afferent projections from top-down pathways or neuromodulation onto sensory cortex. However, the computational mechanisms mediating the effects of such projections are not known. Here, we investigate the effects of afferent projections on the information processing speed of cortical circuits. Using a biologically plausible model based on recurrent networks of excitatory and inhibitory neurons arranged in cluster, we classify the effects of cell-type specific perturbations on the circuit’s stimulus-processing capability. We found that perturbations differentially controlled processing speed, leading to counter-intuitive effects such as improved performance with increased input variance. Our theory explains the effects of all perturbations in terms of gain modulation, which controls the timescale of the circuit dynamics. We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where a decrease in single-cell gain during locomotion explained the observed acceleration of visual processing speed. Our results establish a novel theory of cell-type specific perturbations linking connectivity, dynamics, and information processing via gain modulations.

scholarly journals Visual discrimination of optical material properties: a large-scale study

2019 ◽  
Author(s):  
Masataka Sawayama ◽  
Yoshinori Dobashi ◽  
Makoto Okabe ◽  
Kenchi Hosokawa ◽  
Takuya Koumura ◽  
...  
Keyword(s):  
Visual Processing ◽  
Visual Discrimination ◽  
Large Scale ◽  
Discrimination Performance ◽  
Developmental Studies ◽  
Strongly Correlated ◽  
Experimental Conditions ◽  
Response Data ◽  
Spatial Consistency ◽  
Physically Based

AbstractComplex visual processing involved in perceiving the object materials can be better elucidated by taking a variety of research approaches. Sharing stimulus and response data is an effective strategy to make the results of different studies directly comparable and can assist researchers with different backgrounds to jump into the field. Here, we constructed a database containing a variety of material images annotated with visual discrimination performance. We created various material images by using physically-based computer graphics techniques and conducted psychophysical experiments using them in both laboratory and crowdsourcing settings. The observer’s task was to discriminate materials on six dimensions (gloss contrast, gloss sharpness, translucent vs. opaque, metal vs. plastic, metal vs. glass, and glossy vs. painted) with several task difficulties. The illumination consistency and object geometry were also varied. We used a non-verbal procedure (an oddity task) so that our database could be used in diverse cross-cultural, cross-species, clinical, and developmental studies. The results showed that discrimination performance was affected by the illumination condition and object geometry, in agreement with previous studies on gloss perception, although the pattern of effects was slightly different for some material dimensions. We also found that the ability to discriminate the spatial consistency of specular highlights in glossiness perception showed larger individual differences than in other tasks. The results obtained through crowdsourcing were strongly correlated with those obtained in the laboratory, which suggests that our database can be used even when the experimental conditions are not strictly controlled. Several projects using our dataset are underway.

scholarly journals The HCP 7T Retinotopy Dataset: Description and pRF Analysis

2018 ◽  
Author(s):  
Noah C. Benson ◽  
Keith W. Jamison ◽  
Michael J. Arcaro ◽  
An Vu ◽  
Matthew F. Glasser ◽  
...  
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Individual Variability ◽  
7 Tesla ◽  
Individual Subject ◽  
Retinotopic Mapping ◽  
Human Connectome Project ◽  
The Brain ◽  
Model Based Analysis

AbstractAbout a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as ‘retinotopic mapping’. One of the datasets collected by the Human Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other HCP measures acquired in these same participants.

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