scholarly journals A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance

2005 ◽  
Vol 25 (43) ◽  
pp. 10049-10060 ◽  
Author(s):  
H. Nover
2020 ◽  
Vol 123 (2) ◽  
pp. 682-694 ◽  
Author(s):  
Jacob L. Yates ◽  
Leor N. Katz ◽  
Aaron J. Levi ◽  
Jonathan W. Pillow ◽  
Alexander C. Huk

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.


2017 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Benjamin J. Allitt ◽  
Maureen A. Hagan ◽  
Nicholas S. Price ◽  
Ramesh Rajan ◽  
...  

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.


2018 ◽  
Vol 9 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Fernanda da C. e C. Faria ◽  
Jorge Batista ◽  
Helder Araújo

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.


2013 ◽  
Vol 591 (22) ◽  
pp. 5671-5690 ◽  
Author(s):  
Saba Gharaei ◽  
Chris Tailby ◽  
Selina S. Solomon ◽  
Samuel G. Solomon

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