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Open AccessJournal ArticleDOI

Motion detection: cells, circuits and algorithms

Giordano Ramos-Traslosheros, +2 more
- 25 May 2018 - 
- Vol. 24, Iss: 2
TLDR
In this article, the authors describe classical models of motion detection and introduce core motion detecting circuits in Drosophila, showing that the microcircuits that process elementary motion are more complex than anticipated.
Abstract
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.

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Citations
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Journal ArticleDOI

Populations of local direction–selective cells encode global motion patterns generated by self-motion

TL;DR: A population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal’s movement.
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The physiological basis for contrast opponency in motion computation in Drosophila.

TL;DR: In this paper, the authors reveal the biological substrate for motion computation in the OFF pathway in Drosophila, showing how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.
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Spatio-temporal Memory for Navigation in a Mushroom Body Model

TL;DR: This work proposes that interconnections between Kenyon cells in the Mushroom Body could encode spatio-temporal memory of visual motion experienced when moving along a route, and proposes a biologically constrained spiking neural network model based on simplified MB architecture and using modified leaky integrate-and-fire neurons.
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Distinct expression of potassium channels regulates visual response properties of lamina neurons in Drosophila melanogaster

TL;DR: It is shown that the voltage-gated Ka channels Shaker and Shal contribute to the response properties of the major OFF pathway input L2, and a role for Ka channels in determining the signal-processing characteristics of a specific cell type in the visual system is revealed.
Book ChapterDOI

Spatio-Temporal Memory for Navigation in a Mushroom Body Model

TL;DR: In this paper, a biologically constrained spiking neural network model was proposed to encode spatio-temporal memory of visual motion experienced when moving along a route in an event-based camera mounted on a robot.
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Related Papers (5)
Trending Questions (1)
What are the theories related to motion detection?

The paper discusses two theories related to motion detection: the HRC model, which proposes feedforward amplification of preferred direction signals, and the BLM model, which suggests null direction inhibition.