scholarly journals Sensitivity of Neurons in the Middle Temporal Area of Marmoset Monkeys to Random Dot Motion

2017 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Benjamin J. Allitt ◽  
Maureen A. Hagan ◽  
Nicholas S. Price ◽  
Ramesh Rajan ◽  
...  
Keyword(s):  
Primate Species ◽  
Neural Basis ◽  
Temporal Area ◽  
Middle Temporal ◽  
Mt Neurons ◽  
Wide Range ◽  
Middle Temporal Area ◽  
Dot Motion ◽  
Marmoset Monkeys ◽  
Random Dot Motion

AbstractNeurons in the Middle Temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of “neurometric” thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide anesthetized marmoset monkeys, a species which has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds, and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT, and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments.New and NoteworthyWe report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and that the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

scholarly journals Sensitivity of neurons in the middle temporal area of marmoset monkeys to random dot motion

2017 ◽  
Vol 118 (3) ◽  
pp. 1567-1580 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Benjamin J. Allitt ◽  
Maureen A. Hagan ◽  
Nicholas S. C. Price ◽  
Ramesh Rajan ◽  
...  
Keyword(s):  
Primate Species ◽  
Neural Basis ◽  
Temporal Area ◽  
Middle Temporal ◽  
Mt Neurons ◽  
Wide Range ◽  
Middle Temporal Area ◽  
Dot Motion ◽  
Marmoset Monkeys ◽  
Random Dot Motion

Neurons in the middle temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of “neurometric” thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide-anesthetized marmoset monkeys, a species that has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments. NEW & NOTEWORTHY We report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

scholarly journals Biologically inspired computational modeling of motion based on middle temporal area

2018 ◽  
Vol 9 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Fernanda da C. e C. Faria ◽  
Jorge Batista ◽  
Helder Araújo
Keyword(s):  
Receptive Fields ◽  
Biologically Inspired ◽  
Significant Information ◽  
Temporal Area ◽  
Middle Temporal ◽  
Complex Cells ◽  
Motion Computation ◽  
Reciprocal Connections ◽  
Middle Temporal Area ◽  
Last Stage

Abstract This paper describes a bio-inspired algorithm for motion computation based on V1 (Primary Visual Cortex) andMT (Middle Temporal Area) cells. The behavior of neurons in V1 and MT areas contain significant information to understand the perception of motion. From a computational perspective, the neurons are treated as two dimensional filters to represent the receptive fields of simple cells that compose the complex cells. A modified elaborated Reichardt detector, adding an output exponent before the last stage followed by a re-entry stage of modulating feedback from MT, (reciprocal connections of V1 and MT) in a hierarchical framework, is proposed. The endstopped units, where the receptive fields of cells are surrounded by suppressive regions, are modeled as a divisive operation. MT cells play an important role for integrating and interpreting inputs from earlier-level (V1).We fit a normalization and a pooling to find the most active neurons for motion detection. All steps employed are physiologically inspired processing schemes and need some degree of simplification and abstraction. The results suggest that our proposed algorithm can achieve better performance than recent state-of-the-art bio-inspired approaches for real world images.

scholarly journals A quantitative framework for motion visibility in human cortex

2018 ◽  
Vol 120 (4) ◽  
pp. 1824-1839 ◽  
Author(s):  
Daniel Birman ◽  
Justin L. Gardner
Keyword(s):  
Blood Oxygen Level Dependent ◽  
Cortical Response ◽  
Cortical Representation ◽  
Perceptual Decision Making ◽  
Neural Basis ◽  
Temporal Area ◽  
Visual Areas ◽  
Motion Contrast ◽  
Dot Motion ◽  
Sensory Neural

Despite the central use of motion visibility to reveal the neural basis of perception, perceptual decision making, and sensory inference there exists no comprehensive quantitative framework establishing how motion visibility parameters modulate human cortical response. Random-dot motion stimuli can be made less visible by reducing image contrast or motion coherence, or by shortening the stimulus duration. Because each of these manipulations modulates the strength of sensory neural responses they have all been extensively used to reveal cognitive and other nonsensory phenomena such as the influence of priors, attention, and choice-history biases. However, each of these manipulations is thought to influence response in different ways across different cortical regions and a comprehensive study is required to interpret this literature. Here, human participants observed random-dot stimuli varying across a large range of contrast, coherence, and stimulus durations as we measured blood-oxygen-level dependent responses. We developed a framework for modeling these responses that quantifies their functional form and sensitivity across areas. Our framework demonstrates the sensitivity of all visual areas to each parameter, with early visual areas V1–V4 showing more parametric sensitivity to changes in contrast and V3A and the human middle temporal area to coherence. Our results suggest that while motion contrast, coherence, and duration share cortical representation, they are encoded with distinct functional forms and sensitivity. Thus, our quantitative framework serves as a reference for interpretation of the vast perceptual literature manipulating these parameters and shows that different manipulations of visibility will have different effects across human visual cortex and need to be interpreted accordingly. NEW & NOTEWORTHY Manipulations of motion visibility have served as a key tool for understanding the neural basis for visual perception. Here we measured human cortical response to changes in visibility across a comprehensive range of motion visibility parameters and modeled these with a quantitative framework. Our quantitative framework can be used as a reference for linking human cortical response to perception and underscores that different manipulations of motion visibility can have greatly different effects on cortical representation.

scholarly journals Functional specialization in the middle temporal area for smooth pursuit initiation

2019 ◽  
Vol 2 ◽  
pp. 6 ◽  
Author(s):  
Shahab Bakhtiari ◽  
Christopher C. Pack
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Sensory Information ◽  
Target Velocity ◽  
Multiple Sources ◽  
Temporal Area ◽  
Middle Temporal ◽  
Mt Neurons ◽  
Pursuit Eye Movements ◽  
Pursuit Initiation

Smooth pursuit eye movements have frequently been used to model sensorimotor transformations in the brain. In particular, the initiation phase of pursuit can be understood as a transformation of a sensory estimate of target velocity into an eye rotation. Despite careful laboratory controls on the stimulus conditions, pursuit eye movements are frequently observed to exhibit considerable trial-to-trial variability. In theory, this variability can be caused by the variability in sensory representation of target motion, or by the variability in the transformation of sensory information to motor commands. Previous work has shown that neural variability in the middle temporal (MT) area is likely propagated to the oculomotor command, and there is evidence to suggest that the magnitude of this variability is sufficient to account for the variability of pursuit initiation. This line of reasoning presumes that the MT population is homogeneous with respect to its contribution to pursuit initiation.  At the same time, there is evidence that pursuit initiation is strongly linked to a subpopulation of MT neurons (those with strong surround suppression) that collectively generate less motor variability. To distinguish between these possibilities, we have combined human psychophysics, monkey electrophysiology, and computational modeling to examine how the pursuit system reads out the MT population during pursuit initiation. We find that the psychophysical data are best accounted for by a model that gives stronger weight to surround-suppressed MT neurons, suggesting that variability in the initiation of pursuit could arise from multiple sources along the sensorimotor transformation.

scholarly journals A simple linear readout of MT supports motion direction-discrimination performance

2020 ◽  
Vol 123 (2) ◽  
pp. 682-694 ◽  
Author(s):  
Jacob L. Yates ◽  
Leor N. Katz ◽  
Aaron J. Levi ◽  
Jonathan W. Pillow ◽  
Alexander C. Huk
Keyword(s):  
Temporal Dynamics ◽  
Temporal Integration ◽  
Discrimination Performance ◽  
Neuron Activity ◽  
Motion Direction ◽  
Temporal Area ◽  
Middle Temporal ◽  
Motion Discrimination ◽  
Temporal Properties ◽  
Middle Temporal Area

Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimination behavior or to approach optimal performance. In this study, we evaluated the performance of various neurally plausible decoders, trained to discriminate motion direction from small ensembles of simultaneously recorded MT neurons. We found that decoding the stimulus without knowledge of the interneuronal correlations was sufficient to match an optimal (correlation aware) decoder. Additionally, a decoder could match the psychophysical performance of the animals with flat integration of up to half the stimulus and inherited temporal dynamics from the time-varying MT responses. These results demonstrate that simple, linear decoders operating on small ensembles of neurons can match both psychophysical performance and optimal sensitivity without taking correlations into account and that such simple read-out mechanisms can exhibit complex temporal properties inherited from the sensory dynamics themselves. NEW & NOTEWORTHY Motion perception depends on the ability to decode the activity of neurons in the middle temporal area. Theoretically optimal decoding requires knowledge of the sensitivity of neurons and interneuronal correlations. We report that a simple correlation-blind decoder performs as well as the optimal decoder for coarse motion discrimination. Additionally, the decoder could match the psychophysical performance with moderate temporal integration and dynamics inherited from sensory responses.

Sign in / Sign up

Export Citation Format

Share Document