HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Higher Cognitive Functions in Bio-Inspired Artificial Intelligence

Frédéric Alexandre 1 Xavier Hinaut 1 Nicolas Rougier 1 Thierry Viéville 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : Major algorithms from artificial intelligence (AI) lack higher cognitive functions such as problem solving and reasoning. By studying how these functions operate in the brain, we can develop a biologically informed cognitive computing; transferring our knowledge about architectural and learning principles in the brain to AI. Digital techniques in artificial intelligence (AI) have been making enormous progress and offer impressive performance for the cognitive functions they model. Deep learning has been primarily developed for pattern matching, and extensions like Long Short Term Memory (LSTM) networks can identify and predict temporal sequences. Adaptations to other domains, such as deep reinforcement learning, allow complex strategies of decision-making to be learnt to optimise cumulated rewards.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/hal-03189215
Contributor : Frédéric Alexandre Connect in order to contact the contributor
Submitted on : Friday, April 2, 2021 - 9:59:47 PM
Last modification on : Friday, January 21, 2022 - 3:10:41 AM
Long-term archiving on: : Saturday, July 3, 2021 - 7:01:19 PM

File

03-alexandre - final.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03189215, version 1

Citation

Frédéric Alexandre, Xavier Hinaut, Nicolas Rougier, Thierry Viéville. Higher Cognitive Functions in Bio-Inspired Artificial Intelligence. ERCIM News, ERCIM, 2021, Special topic "Brain inspired computing", 125. ⟨hal-03189215⟩

Share

Metrics

Record views

79

Files downloads

124