Search anything and hit enter
  • Teams
  • Members
  • Projects
  • Events
  • Calls
  • Jobs
  • publications
  • Software
  • Tools
  • Network
  • Equipment

A little guide for advanced search:

  • Tip 1. You can use quotes "" to search for an exact expression.
    Example: "cell division"
  • Tip 2. You can use + symbol to restrict results containing all words.
    Example: +cell +stem
  • Tip 3. You can use + and - symbols to force inclusion or exclusion of specific words.
    Example: +cell -stem
e.g. searching for members in projects tagged cancer
Search for
Count
IN
OUT
Content 1
  • member
  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
  • Graduate Student
  • Lab assistant
  • Non-permanent Researcher
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
Content 2
  • member
  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
  • Graduate Student
  • Lab assistant
  • Non-permanent Researcher
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
Search
Go back
Scroll to top
Share
© Thomas Gregor
The image shows a Drosophila embryo 2 hr after fertilization, with nuclei at the surface fluorescently labeled for Bicoid protein (blue), Hunchback protein (green), and DNA (red). Using two-photon microscopy these embryos were imaged to quantitatively characterize the dynamics and precision of how morphogen molecules communicate positional information to individual nuclei. In this example, the shallow Bicoid gradient generates a sharp Hunchback boundary (enlarged in the background), partitioning the embryo in half. This input/output relationship is quantitatively represented in the foreground (yellow), where each dot specifies the Bicoid concentration (horizontal axis) and Hunchback concentration (vertical axis) measured in a single nucleus. The results indicate that the precision with which the embryo interprets and locates this boundary is very high, approaching limits set by simple physical principles.
Publication : Nature

Glider soaring via reinforcement learning in the field

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Nature - 19 Sep 2018

Reddy G, Wong-Ng J, Celani A, Sejnowski TJ, Vergassola M

Link to Pubmed [PMID] – 30232456

Nature 2018 10;562(7726):236-239

Soaring birds often rely on ascending thermal plumes (thermals) in the atmosphere as they search for prey or migrate across large distances. The landscape of convective currents is rugged and shifts on timescales of a few minutes as thermals constantly form, disintegrate or are transported away by the wind. How soaring birds find and navigate thermals within this complex landscape is unknown. Reinforcement learning provides an appropriate framework in which to identify an effective navigational strategy as a sequence of decisions made in response to environmental cues. Here we use reinforcement learning to train a glider in the field to navigate atmospheric thermals autonomously. We equipped a glider of two-metre wingspan with a flight controller that precisely controlled the bank angle and pitch, modulating these at intervals with the aim of gaining as much lift as possible. A navigational strategy was determined solely from the glider’s pooled experiences, collected over several days in the field. The strategy relies on on-board methods to accurately estimate the local vertical wind accelerations and the roll-wise torques on the glider, which serve as navigational cues. We establish the validity of our learned flight policy through field experiments, numerical simulations and estimates of the noise in measurements caused by atmospheric turbulence. Our results highlight the role of vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds and provide a navigational strategy that is directly applicable to the development of autonomous soaring vehicles.

https://www.ncbi.nlm.nih.gov/pubmed/30232456