Computation of motion direction in the vertebrate retina

e-Neuroforum ◽  
2012 ◽  
Vol 18 (3) ◽  
Author(s):  
T. Euler ◽  
S.E. Hausselt
Keyword(s):  
Large Scale ◽  
Petri Dish ◽  
Cell Types ◽  
Direction Selectivity ◽  
Dendritic Morphology ◽  
Image Motion ◽  
Target Area ◽  
Motion Direction ◽  
Preferred Direction ◽  
The Brain

AbstractHow direction of image motion is detected as early as at the level of the vertebrate eye has been intensively studied in retina research. Although the first direction-selective (DS) ret­inal ganglion cells were already described in the 1960s and have since then been in the fo­cus of many studies, scientists are still puz­zled by the intricacy of the neuronal circuits and computational mechanisms underlying retinal direction selectivity. The fact that the retina can be easily isolated and studied in a Petri dish-by presenting light stimuli while recording from the various cell types in the retinal circuits-in combination with the ex­tensive anatomical, molecular and physiolog­ical knowledge about this part of the brain presents a unique opportunity for studying this intriguing visual circuit in detail. This ar­ticle provides a brief overview of the histo­ry of research on retinal direction selectivi­ty, but then focuses on the past decade and the progress achieved, in particular driven by methodological advances in optical record­ing techniques, molecular genetics approach­es and large-scale ultrastructural reconstruc­tions. As it turns out, retinal direction selec­tivity is a complex, multi-tiered computation, involving dendrite-intrinsic mechanisms as well as several types of network interactions on the basis of highly selective, likely genet­ically predetermined synaptic connectivi­ty. Moreover, DS ganglion cell types appear to be more diverse than previously thought, differing not only in their preferred direction and response polarity, but also in physiology, DS mechanism, dendritic morphology and, importantly, the target area of their projec­tions in the brain.

scholarly journals Weighting neurons by selectivity produces near-optimal population codes

2019 ◽  
Vol 121 (5) ◽  
pp. 1924-1937
Author(s):  
Elizabeth Zavitz ◽  
Nicholas S. C. Price
Keyword(s):  
A Priori ◽  
Maximum Level ◽  
Optimization Methods ◽  
Neuronal Population ◽  
Direction Selectivity ◽  
Motion Direction ◽  
Temporal Area ◽  
Linear Discriminant ◽  
Preferred Direction

Perception is produced by “reading out” the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons’ spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets ( Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron’s tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron’s preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron’s tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher’s linear discriminant) led to only marginally better performance than decoders based purely on a neuron’s preferred direction and selectivity.

scholarly journals Origins of direction selectivity in the primate retina

2021 ◽  
Author(s):  
Yeon Jin Kim ◽  
Beth Peterson ◽  
Joanna Crook ◽  
Hannah Joo ◽  
Jiajia Wu ◽  
...  
Keyword(s):  
Neural Coding ◽  
Amacrine Cell ◽  
Cell Types ◽  
Macaque Monkey ◽  
Direction Selectivity ◽  
Bipolar Cells ◽  
Motion Processing ◽  
Motion Direction ◽  
Motion Sensitivity ◽  
Starburst Amacrine Cell

Abstract From mouse to primate, there is a striking discontinuity in our current understanding of the neural coding of motion direction. In non-primate mammals, directionally selective cell types and circuits are a signature feature of the retina, situated at the earliest stage of the visual process1,2. In primates, by contrast, direction selectivity is a hallmark of motion processing areas in visual cortex3,4, but has not been found in the retina, despite significant effort5,6. Here we combined functional recordings of light-evoked responses and connectomic reconstruction to identify diverse direction-selective cell types in the macaque monkey retina with distinctive physiological properties and synaptic motifs. This circuitry includes an ON-OFF ganglion cell type, a spiking, ON-OFF poly-axonal amacrine cell and the starburst amacrine cell, all of which show direction selectivity. Moreover, we found unexpectedly that macaque starburst cells possess a strong, non-GABAergic, antagonistic surround mediated by input from excitatory bipolar cells that is critical for the generation of radial motion sensitivity in these cells. Our findings open a new door to investigation of a novel circuitry that computes motion direction in the primate visual system.

scholarly journals How fly neurons compute the direction of visual motion

2019 ◽  
Vol 206 (2) ◽  
pp. 109-124 ◽  
Author(s):  
Alexander Borst ◽  
Jürgen Haag ◽  
Alex S. Mauss
Keyword(s):  
Information Needs ◽  
Visual Motion ◽  
Fruit Fly ◽  
Direction Selectivity ◽  
Image Motion ◽  
Exact Nature ◽  
Motion Information ◽  
Preferred Direction ◽  
Direct Excitation ◽  
Directional Motion

Abstract Detecting the direction of image motion is a fundamental component of visual computation, essential for survival of the animal. However, at the level of individual photoreceptors, the direction in which the image is shifting is not explicitly represented. Rather, directional motion information needs to be extracted from the photoreceptor array by comparing the signals of neighboring units over time. The exact nature of this process as implemented in the visual system of the fruit fly Drosophila melanogaster has been studied in great detail, and much progress has recently been made in determining the neural circuits giving rise to directional motion information. The results reveal the following: (1) motion information is computed in parallel ON and OFF pathways. (2) Within each pathway, T4 (ON) and T5 (OFF) cells are the first neurons to represent the direction of motion. Four subtypes of T4 and T5 cells exist, each sensitive to one of the four cardinal directions. (3) The core process of direction selectivity as implemented on the dendrites of T4 and T5 cells comprises both an enhancement of signals for motion along their preferred direction as well as a suppression of signals for motion along the opposite direction. This combined strategy ensures a high degree of direction selectivity right at the first stage where the direction of motion is computed. (4) At the subsequent processing stage, tangential cells spatially integrate direct excitation from ON and OFF-selective T4 and T5 cells and indirect inhibition from bi-stratified LPi cells activated by neighboring T4/T5 terminals, thus generating flow-field-selective responses.

scholarly journals Mechanism of Motion Direction Detection Based on Barlow’s Retina Inhibitory Scheme in Direction-Selective Ganglion Cells

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1663
Author(s):  
Mianzhe Han ◽  
Yuki Todo ◽  
Zheng Tang
Keyword(s):  
Ganglion Cells ◽  
Direction Selectivity ◽  
Detection Accuracy ◽  
Local Motion ◽  
Motion Direction ◽  
Preferred Direction ◽  
Directional Detection ◽  
Direction Detection ◽  
Series Of Experiments ◽  
Better Than

Previous studies have reported that directionally selective ganglion cells respond strongly in their preferred direction, but are only weakly excited by stimuli moving in the opposite null direction. Various studies have attempted to elucidate the mechanisms underlying direction selectivity with cellular basis. However, these studies have not elucidated the mechanism underlying motion direction detection. In this study, we propose the mechanism based on Barlow’s inhibitory scheme for motion direction detection. We described the local motion-sensing direction-selective neurons. Next, this model was used to construct the two-dimensional multi-directional detection neurons which detect the local motion directions. The information of local motion directions was finally used to infer the global motion direction. To verify the validity of the proposed mechanism, we conducted a series of experiments involving a dataset with a number of images. The proposed mechanism exhibited good performance in all experiments with high detection accuracy. Furthermore, we compare the performance of our proposed system and traditional Convolution Neural Network (CNN) on motion direction prediction. It is found that the performance of our system is much better than that of CNN in terms of accuracy, calculation speed and cost.

scholarly journals The Most Informative Neural Code Accounts For Population Heterogeneity

2017 ◽  
Author(s):  
Elizabeth Zavitz ◽  
Nicholas SC Price
Keyword(s):  
Machine Learning ◽  
Maximum Level ◽  
Direction Selectivity ◽  
Machine Learning Algorithms ◽  
Population Heterogeneity ◽  
Motion Direction ◽  
Temporal Area ◽  
Firing Rates ◽  
Preferred Direction

AbstractPerception is produced by ‘reading out’ the representation of a sensory stimulus contained in the firing rates of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly-weighted sum of the neurons’ firing rates. This approach is popular because of its biological validity: weights in a computational decoder are analogous to synaptic strengths. For neurons recorded in vivo, weights are highly variable when derived through machine learning methods, but it is unclear what neuronal properties explain this variability, and how the variability affects decoding performance. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets (Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in one of twelve different directions. We found that high gain and direction selectivity both predicted that a neuron would be weighted more highly in an optimised decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron’s tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron’s preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights.New & NoteworthyWe examined which aspects of a neuron’s tuning account for its contribution to sensory coding. Strongly direction-selective neurons were weighted most highly by machine learning algorithms trained to discriminate motion direction. Models with a priori defined decoding weights demonstrate that the learned weighting scheme causally improved direction representation by a neuronal population. Optimising decoders (using machine learning) lead to only marginally better performance than decoders based purely on a neuron’s preferred direction and selectivity.

Passive Dendritic Trees

1998 ◽  
Author(s):  
Christof Koch
Keyword(s):  
Nervous System ◽  
Morphological Structure ◽  
Pyramidal Cells ◽  
Quantitative Relationship ◽  
Cell Types ◽  
Dendritic Morphology ◽  
Granule Cell Layer ◽  
Dendritic Trees ◽  
A Cell ◽  
The Brain

The previous chapter dealt with the solution of the cable equation in response to current pulses and steps within a single unbranched cable. However, real nerve cells possess highly branched and extended dendritic trees with quite distinct morphologies. Figure 3.1 illustrates the fantastic variety of dendritic trees found throughout the animal kingdom, ranging from neurons in the locust to human brain cells and cells from many different parts of the nervous system. Some of these cells are spatially compact, such as retinal amacrine cells, which are barely one-fifth of a millimeter across, while some cells have immense dendritic trees, such as α motoneurones in the spinal cord extending across several millimeters. Yet, in all cases, neurons are very tightly packed: in vertebrates, peak density appears to be reached in the granule cell layer of the human cerebellum with around 5 million cells per cubic millimeter (Braitenberg and Atwood, 1958) while the packing density of the cells filling the 0.25 mm3 nervous system of the housefly Musca domestica is around 1.2 million cells per cubic millimeter (Strausfeld, 1976). The dendritic arbor of some cell types encompasses a spherical volume, such as for thalamic relay cells, while other cells, such as the cerebellar Purkinje cell, fill a thin slablike volume with a width less than one-tenth of their extent. Different cell types do not appear at random in the brain but are unique to specific parts of the brain. By far the majority of excitatory cells in the cortex are the pyramidal cells. Yet even within this class, considerable diversity exists. But why this diversity of shapes? To what extent do these quite distinct dendritic architectures reflect differences in their roles in information processing and computation? What influence does the dendritic morphology have on the electrical properties of the cell, or, in other words, what is the relationship between the morphological structure of a cell and its electrical function? One of the few cases where a quantitative relationship between form and some aspect of neuronal function has been established is the retinal neurons.

scholarly journals Dynamic Instability of Dendrite Tips Generates the Highly Branched Morphologies of Sensory Neurons

2021 ◽  
Author(s):  
Sonal Shree ◽  
Sabyasachi Sutradhar ◽  
Olivier Trottier ◽  
Yuhai Tu ◽  
Xin Liang ◽  
...  
Keyword(s):  
Sensory Neurons ◽  
Stochastic Dynamics ◽  
Dynamic Instability ◽  
Neuronal Cell ◽  
Cell Types ◽  
Dendritic Morphology ◽  
Dendrite Morphology ◽  
Agent Based ◽  
Neuronal Dendrites ◽  
The Brain

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.

Temporal Dynamics of Direction Tuning in Motion-Sensitive Macaque Area MT

2005 ◽  
Vol 93 (4) ◽  
pp. 2104-2116 ◽  
Author(s):  
János A. Perge ◽  
Bart G. Borghuis ◽  
Roger J. E. Bours ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Temporal Dynamics ◽  
Random Sequence ◽  
Nonlinear Effects ◽  
Direction Selectivity ◽  
Second Phase ◽  
Motion Direction ◽  
Area Mt ◽  
Mt Neurons ◽  
Static Nonlinearity ◽  
Preferred Direction

We studied the temporal dynamics of motion direction sensitivity in macaque area MT using a motion reverse correlation paradigm. Stimuli consisted of a random sequence of motion steps in eight different directions. Cross-correlating the stimulus with the resulting neural activity reveals the temporal dynamics of direction selectivity. The temporal dynamics of direction selectivity at the preferred speed showed two phases along the time axis: one phase corresponding to an increase in probability for the preferred direction at short latencies and a second phase corresponding to a decrease in probability for the preferred direction at longer latencies. The strength of this biphasic behavior varied between neurons from weak to very strong and was uniformly distributed. Strong biphasic behavior suggests optimal responses for motion steps in the antipreferred direction followed by a motion step in the preferred direction. Correlating spikes to combinations of motion directions corroborates this distinction. The optimal combination for weakly biphasic cells consists of successive steps in the preferred direction, whereas for strongly biphasic cells, it is a reversal of directions. Comparing reverse correlograms to combinations of stimuli to predictions based on correlograms for individual directions revealed several nonlinear effects. Correlations for successive presentations of preferred directions were smaller than predicted, which could be explained by a static nonlinearity (saturation). Correlations to pairs of (nearly) opposite directions were larger than predicted. These results show that MT neurons are generally more responsive when sudden changes in motion directions occur, irrespective of the preferred direction of the neurons. The latter nonlinearities cannot be explained by a simple static nonlinearity at the output of the neuron, but most likely reflect network interactions.

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