Synopsis of Social media discussions

The discussions primarily promote upcoming events and refer to the publication as an interesting advancement in understanding neural dynamics and animal-inspired AI, using phrases like 'emergent behaviour' and 'neural mechanisms,' which reflect curiosity and recognition of its importance.

A
Agreement
Moderate agreement

Most posts endorse the research and its relevance, with references to the paper and talks that suggest support.

I
Interest
High level of interest

Discussions highlight strong interest in the topic, mentioning seminars and the importance of the findings.

E
Engagement
Moderate level of engagement

Posts show moderate engagement by encouraging further exploration of the paper and its conference presentation.

I
Impact
Moderate level of impact

The discussions reflect some recognition of the potential significance, but mostly serve to promote awareness without deep analysis.

Social Mentions

YouTube

2 Videos

Twitter

7 Posts

News

11 Articles

Reddit

2 Posts

Metrics

Video Views

29

Total Likes

8

Extended Reach

20,069

Social Features

22

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

AI-based Odour Tracking: Neural Dynamics and Emergent Behaviour

AI-based Odour Tracking: Neural Dynamics and Emergent Behaviour

Researchers used deep reinforcement learning to train AI agents to track odour plumes, mimicking insect behaviour. The study offers insights into neural mechanisms, memory, and environmental adaptation in odour source localization.

August 17, 2023

18 views


Neural Dynamics of Artificial Agents Tracking Odour Plumes

Neural Dynamics of Artificial Agents Tracking Odour Plumes

This video explores how artificial agents, trained with deep reinforcement learning, emulate insect behaviour in tracking odour plumes under variable environmental conditions, revealing insights into neural processing and memory requirements.

October 5, 2023

11 views


  • Wilka Carvalho
    @cogscikid (Twitter)

    RT @rlbrainseminar: Next meeting is Thurs, April 18 at 12:15 PM in WAB 236! The speaker is Satpreet Singh (@tweetsatpreet) from the Rajan L…
    view full post

    April 17, 2024

    3

  • Satpreet Singh
    @tweetsatpreet (Twitter)

    RT @rlbrainseminar: Next meeting is Thurs, April 18 at 12:15 PM in WAB 236! The speaker is Satpreet Singh (@tweetsatpreet) from the Rajan L…
    view full post

    April 16, 2024

    3

  • Kanaka Rajan
    @KanakaRajanPhD (Twitter)

    RT @rlbrainseminar: Next meeting is Thurs, April 18 at 12:15 PM in WAB 236! The speaker is Satpreet Singh (@tweetsatpreet) from the Rajan L…
    view full post

    April 16, 2024

    3

  • RL and the Brain seminar @ HMS
    @rlbrainseminar (Twitter)

    For more, see Satpreet's talk or his recent paper in Nature Machine Intelligence: https://t.co/vfZ0qBoUMf
    view full post

    April 16, 2024

  • RL and the Brain seminar @ HMS
    @rlbrainseminar (Twitter)

    Next meeting is Thurs, April 18 at 12:15 PM in WAB 236! The speaker is Satpreet Singh (@tweetsatpreet) from the Rajan Lab (@KanakaRajanPhD). His talk is titled "Emergent behaviour and neural dynamics in artificial agents tracking odour plumes".
    view full post

    April 16, 2024

    8

    3

  • Ordo Fraterna Fibonacci
    @OrdoFibonacci (Twitter)

    Emergent behaviour and neural dynamics in artificial agents tracking odour plumes | Nature Machine Intelligence https://t.co/6UHioKiM73
    view full post

    February 10, 2023

  • humanrobotcollective
    @humanrobotcoll2 (Twitter)

    "Emergent behaviour and neural dynamics in artificial agents tracking odour plumes" by Bingni W. Brunton (Nature Machine Intelligence) (https://t.co/U2tijILnXC) https://t.co/g7QxQR3UAP
    view full post

    January 26, 2023

Abstract Synopsis

  • Tracking an odour plume to find its source is challenging, but flying insects excel at this over long distances, guided by complex neural mechanisms.
  • Researchers utilized an in silico approach, training artificial recurrent neural networks with deep reinforcement learning to replicate the behaviour of insects in tracking simulated odour plumes that reflect real environmental conditions.
  • The study reveals that these AI agents show behaviour similar to insects and discover effective ways to process important information, providing insights into memory needs and neural dynamics related to tracking odour in varying wind directions.