scholarly journals A minimal synaptic model for direction selective neurons in Drosophila

2019 ◽  
Author(s):  
Jacob A. Zavatone-Veth ◽  
Bara A. Badwan ◽  
Damon A. Clark
Keyword(s):  
Visual Motion ◽  
Fine Tuning ◽  
Physiological Data ◽  
Natural Scenes ◽  
Levels Of Abstraction ◽  
Response Properties ◽  
Classical Models ◽  
Correlator Model ◽  
Functional Circuitry ◽  
Synaptic Model

AbstractVisual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomical and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results, but have rarely been compared across experiments. Here we construct a minimal, biophysically inspired synaptic model for Drosophila’s first-order direction-selective T4 cells using the wealth of available anatomical and physiological data. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where our understanding must be improved.

scholarly journals Motion detection: cells, circuits and algorithms

Neuroforum ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. A61-A72 ◽  
Author(s):  
Giordano Ramos-Traslosheros ◽  
Miriam Henning ◽  
Marion Silies
Keyword(s):  
Motion Detection ◽  
Visual Motion ◽  
Motion Cues ◽  
Detection Algorithms ◽  
Preferred Direction ◽  
Direct Measurements ◽  
Classical Models ◽  
Light Signals ◽  
Circuit Components ◽  
Visual Motion Cues

Abstract Many animals use visual motion cues to inform different behaviors. The basis for motion detection is the comparison of light signals over space and time. How a nervous system performs such spatiotemporal correlations has long been considered a paradigmatic neural computation. Here, we will first describe classical models of motion detection and introduce core motion detecting circuits in Drosophila. Direct measurements of the response properties of the first direction-selective cells in the Drosophila visual system have revealed new insights about the implementation of motion detection algorithms. Recent data suggest a combination of two mechanisms, a nonlinear enhancement of signals moving into the preferred direction, as well as a suppression of signals moving into the opposite direction. These findings as well as a functional analysis of the circuit components have shown that the microcircuits that process elementary motion are more complex than anticipated. Building on this, we have the opportunity to understand detailed properties of elementary, yet intricate microcircuits.

scholarly journals Visual Distance Estimation in Static Compared to Moving Virtual Scenes

2006 ◽  
Vol 9 (2) ◽  
pp. 321-331 ◽  
Author(s):  
Harald Frenz ◽  
Markus Lappe
Keyword(s):  
Virtual Environment ◽  
Visual Motion ◽  
Human Subjects ◽  
Distance Estimation ◽  
Visual Space ◽  
Depth Interval ◽  
Natural Scenes ◽  
Virtual Scene ◽  
Visual Distance ◽  
Control Direction

Visual motion is used to control direction and speed of self-motion and time-to-contact with an obstacle. In earlier work, we found that human subjects can discriminate between the distances of different visually simulated self-motions in a virtual scene. Distance indication in terms of an exocentric interval adjustment task, however, revealed linear correlation between perceived and indicated distances but with a profound distance underestimation. One possible explanation for this underestimation is the perception of visual space in virtual environments. Humans perceive visual space in natural scenes as curved, and distances are increasingly underestimated with increasing distance from the observer. Such spatial compression may also exist in our virtual environment. We therefore surveyed perceived visual space in a static virtual scene. We asked observers to compare two horizontal depth intervals, similar to experiments performed in natural space. Subjects had to indicate the size of one depth interval relative to a second interval. Our observers perceived visual space in the virtual environment as compressed, similar to the perception found in natural scenes. However, the nonlinear depth function we found can not explain the observed distance underestimation of visual simulated self-motions in the same environment.

scholarly journals Mouse xenograft models for elucidating drug resistance mechanisms

2015 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Drug Resistance ◽  
Drug Treatment ◽  
Cancer Treatment ◽  
Primary Tumour ◽  
Drug Regimen ◽  
Resistance Mechanisms ◽  
Fine Tuning ◽  
Blood Biomarkers ◽  
Levels Of Abstraction ◽  
Xenograft Models

Divergence of treatment responses to chemotherapy exists across patients (often with underlying mechanisms unknown), with some patients exhibiting worsened outcome upon treatment. Genomic approaches (such as microarray profiling and whole-genome sequencing) hold promise for transforming cancer treatment, particularly, in tailoring drug regimen to specific patients. Nevertheless, formulating effective personalized treatment via surveying the mutational landscape remains difficult, due to current deficiencies in predicting drug sensitivity from genotype. Xenografts, both indirect (via cell line) and direct (from primary tumours), are good physiologic models of cancers. Their utility in informing cancer treatment, however, is constrained by high cost of generating and maintaining genetically modified animals, and the paucity of tissue samples from biopsies. Advent of high throughput biomolecular profiling tools finally enables reading out the expansive molecular fingerprints that encode observed phenotypes in xenografts. Using pheochromocytoma (an adrenal medulla cancer) as example, this short essay provides a broad overview of the scientific and clinical possibilities offered by xenograft models for understanding resistance mechanisms to particular chemotherapeutic regimens, and upon identification of the putative mutations, confirms their functional roles as either oncogenes or tumour suppressors. Additionally, workflow involved in generating a predictive platform, based on non-invasive blood biomarkers, for informing drug treatment options is discussed. Known as an integrated genomic classifier, combination of physiological response of direct xenografts to drug treatment and bioinformatics-enabled correlation of blood biomarkers to observed phenotype at cellular and animal levels, provides the biological basis for predicting patients’ prognosis without invasive biopsy of solid tumours. Elucidation of drug resistance mechanisms entails: (i) recapitulating in vivo tumour behavior using cell lines derived from primary tumour; (ii) identification of aetiological mutations and longitudinal profiling of phenotypic response; and (iii) validation of mutations and phenotype via both knockout mice and direct allogenic xenografts. Biological models seek to recapitulate human physiology at specific levels of abstraction for answering particular questions, but incongruence in phenotype is inevitable. Nevertheless, xenografts (especially those derived from patients, PDTX), are powerful tools for addressing basic science, clinical and treatment-related questions using close functional replicas of patient physiology in an animal model. Residual incompatibility between model and patient response would require the expertise and clinical experience of oncologists for fine-tuning model suggested drug regimen to particular patients.

scholarly journals Orientation tuning depends on spatial frequency in mouse visual cortex

2016 ◽  
Author(s):  
Inbal Ayzenshtat ◽  
Jesse Jackson ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Natural Scenes ◽  
Response Properties ◽  
Layer 2 ◽  
Mouse Visual Cortex

AbstractThe response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.Significance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.Highlights–Orientation selectivity is dependent on spatial frequency.–Asymmetric Gabor model can explain this dependence.

Ventral intraparietal area of the macaque: anatomic location and visual response properties

1993 ◽  
Vol 69 (3) ◽  
pp. 902-914 ◽  
Author(s):  
C. L. Colby ◽  
J. R. Duhamel ◽  
M. E. Goldberg
Keyword(s):  
Visual Motion ◽  
Visual Response ◽  
Intraparietal Sulcus ◽  
Stimulus Motion ◽  
Area Mt ◽  
Temporal Area ◽  
Response Properties ◽  
Preferred Direction ◽  
The Face ◽  
Ventral Intraparietal Area

1. The middle temporal area (MT) projects to the intraparietal sulcus in the macaque monkey. We describe here a discrete area in the depths of the intraparietal sulcus containing neurons with response properties similar to those reported for area MT. We call this area the physiologically defined ventral intraparietal area, or VIP. In the present study we recorded from single neurons in VIP of alert monkeys and studied their visual and oculomotor response properties. 2. Area VIP has a high degree of selectivity for the direction of a moving stimulus. In our sample 72/88 (80%) neurons responded at least twice as well to a stimulus moving in the preferred direction compared with a stimulus moving in the null direction. The average response to stimuli moving in the preferred direction was 9.5 times as strong as the response to stimuli moving in the opposite direction, as compared with 10.9 times as strong for neurons in area MT. 3. Many neurons were also selective for speed of stimulus motion. Quantitative data from 25 neurons indicated that the distribution of preferred speeds ranged from 10 to 320 degrees/s. The degree of speed tuning was on average twice as broad as that reported for area MT. 4. Some neurons (22/41) were selective for the distance at which a stimulus was presented, preferring a stimulus of equivalent visual angle and luminance presented near (within 20 cm) or very near (within 5 cm) the face. These neurons maintained their preference for near stimuli when tested monocularly, suggesting that visual cues other than disparity can support this response. These neurons typically could not be driven by small spots presented on the tangent screen (at 57 cm). 5. Some VIP neurons responded best to a stimulus moving toward the animal. The absolute direction of visual motion was not as important for these cells as the trajectory of the stimulus: the best stimulus was one moving toward a particular point on the face from any direction. 6. VIP neurons were not active in relation to saccadic eye movements. Some neurons (10/17) were active during smooth pursuit of a small target. 7. The predominance of direction and speed selectivity in area VIP suggests that it, like other visual areas in the dorsal stream, may be involved in the analysis of visual motion.

scholarly journals Random Compressed Coding with Neurons

2022 ◽  
Author(s):  
Simone Blanco Malerba ◽  
Mirko Pieropan ◽  
Yoram Burak ◽  
Rava Azeredo da Silveira
Keyword(s):  
Neural Network ◽  
Fine Tuning ◽  
Neural Population ◽  
Dimensional Representation ◽  
Tuning Curves ◽  
Efficient Coding ◽  
Monotonic Functions ◽  
The Neural Network ◽  
Classical Models ◽  
Low Dimensional

Classical models of efficient coding in neurons assume simple mean responses--'tuning curves'--such as bell shaped or monotonic functions of a stimulus feature. Real neurons, however, can be more complex: grid cells, for example, exhibit periodic responses which impart the neural population code with high accuracy. But do highly accurate codes require fine tuning of the response properties? We address this question with the use of a benchmark model: a neural network with random synaptic weights which result in output cells with irregular tuning curves. Irregularity enhances the local resolution of the code but gives rise to catastrophic, global errors. For optimal smoothness of the tuning curves, when local and global errors balance out, the neural network compresses information from a high-dimensional representation to a low-dimensional one, and the resulting distributed code achieves exponential accuracy. An analysis of recordings from monkey motor cortex points to such 'compressed efficient coding'. Efficient codes do not require a finely tuned design--they emerge robustly from irregularity or randomness.

scholarly journals Towards a unified theory of efficient, predictive and sparse coding

2017 ◽  
Author(s):  
Matthew Chalk ◽  
Olivier Marre ◽  
Gašper Tkačik
Keyword(s):  
Sparse Coding ◽  
Visual Motion ◽  
Synergistic Effects ◽  
Predictive Coding ◽  
Low Noise ◽  
Neural Responses ◽  
Unified Framework ◽  
Response Properties ◽  
Efficient Coding ◽  
Special Cases

A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. Several theories have been proposed to this end. “Efficient coding” posits that neural circuits maximise information encoded about their inputs. “Sparse coding” posits that individual neurons respond selectively to specific, rarely occurring, features. Finally, “predictive coding” posits that neurons preferentially encode stimuli that are useful for making predictions. Except in special cases, it is unclear how these theories relate to each other, or what is expected if different coding objectives are combined. To address this question, we developed a unified framework that encompasses these previous theories and extends to new regimes, such as sparse predictive coding. We explore cases when different coding objectives exert conflicting or synergistic effects on neural response properties. We show that predictive coding can lead neurons to either correlate or decorrelate their inputs, depending on presented stimuli, while (at low-noise) efficient coding always predicts decorrelation. We compare predictive versus sparse coding of natural movies, showing that the two theories predict qualitatively different neural responses to visual motion. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to a multiplicity of functional goals performed by different cell types and/or circuits.

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