Alearning_fullpaper_rev2.pdf (835.25 kB)
Input-modulation as an alternative to conventional learning strategies
Animals use various strategies for learning stimulus-reward associations. Computational methods that mimic animal behaviour most commonly interpret learning as a high level phenomenon, in which the pairing of stimulus and reward leads to plastic changes in the final output layers where action selection takes place. Here, we present an alternative input-modulation strategy for forming simple stimulus-response associations based on reward. Our model is motivated by experimental evidence on modulation of early brain regions by reward signalling in the honeybee. The model can successfully discriminate dissimilar odours and generalise across similar odours, like bees do. In the most simplified connectionist description, the new input- modulation learning is shown to be asymptotically equivalent to the standard perceptron.
Funding
Odor-background segregation and source localization using fast olfactory processing; G1652; HUMAN FRONTIER SCIENCE PROGRAM (HFSP); RGP0053/2015
Green brain; G0924; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/J019690/1
History
Publication status
- Published
File Version
- Accepted version
Publisher
SpringerExternal DOI
Volume
9886Page range
54-62Pages
8.0Book title
Proceedings of the ICANN 2016 ConferenceISBN
0302-9743Series
Lecture Notes in Computer ScienceDepartment affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes