fbpx Sorting ideas out with an app | Science in the net

Sorting ideas out with an app

Primary tabs

Read time: 2 mins

The mind map allows you to have an overview of an idea, to understand how it is structured and organized, its connections and relationships. By visually summarizing the main properties of a thought or a project, the mind map facilitates its organization, its understanding and memorization.
Mind maps are generally based on hierarchy and associations, and connections can be of 2 types:

- Hierarchical connections (or branches) connecting each element with the previous one
- Associative connections (or associations) connecting elements that are not hierarchically linked together

The key idea is at the center of the mind map and is connected via the hierarchical links to the first-level elements. First-level elements can be connected to second- level elements, second level elements to third-level elements and so on. Elements that are far apart and not hierarchically linked can be connected through associations.
Generally, the graphic layout of all elements of a mental map is radial, but it can also be a herringbone or tree layout.

When you first use of the app it is very useful to read the "Basic Gestures" item in the help (?) in order to understand how to perform basic operations and to experiment with the app. It is important however that you do not save the changes made.
Since the mind map is a useful mnemonic tool, it is important to use colors, symbols, pictures or images when creating each element.
The name of each element should be brief and meaningful.

Video overview and previews of the app:

[video:http://vimeo.com/32367415]

MJ1 MJ2

MJ3 MJ4

MJ6


Scienza in rete è un giornale senza pubblicità e aperto a tutti per garantire l’indipendenza dell’informazione e il diritto universale alla cittadinanza scientifica. Contribuisci a dar voce alla ricerca sostenendo Scienza in rete. In questo modo, potrai entrare a far parte della nostra comunità e condividere il nostro percorso. Clicca sul pulsante e scegli liberamente quanto donare! Anche una piccola somma è importante. Se vuoi fare una donazione ricorrente, ci consenti di programmare meglio il nostro lavoro e resti comunque libero di interromperla quando credi.


prossimo articolo

Why have neural networks won the Nobel Prizes in Physics and Chemistry?

This year, Artificial Intelligence played a leading role in the Nobel Prizes for Physics and Chemistry. More specifically, it would be better to say machine learning and neural networks, thanks to whose development we now have systems ranging from image recognition to generative AI like Chat-GPT. In this article, Chiara Sabelli tells the story of the research that led physicist and biologist John J. Hopfield and computer scientist and neuroscientist Geoffrey Hinton to lay the foundations of current machine learning.

Image modified from the article "Biohybrid and Bioinspired Magnetic Microswimmers" https://onlinelibrary.wiley.com/doi/epdf/10.1002/smll.201704374

The 2024 Nobel Prize in Physics was awarded to John J. Hopfield, an American physicist and biologist from Princeton University, and to Geoffrey Hinton, a British computer scientist and neuroscientist from the University of Toronto, for utilizing tools from statistical physics in the development of methods underlying today's powerful machine learning technologies.