6533b85bfe1ef96bd12bb45e
RESEARCH PRODUCT
A Mushroom Bodies inspired spiking network for classification and sequence learning
Luca PatanéRoland StraussMarco CalíAgnese PorteraPaolo Arenasubject
SequenceBasis (linear algebra)Computer scienceProcess (engineering)business.industryContext (language use)Crystal latticesComplex dynamicsMushroom bodiesArtificial intelligenceSequence learningCrystal lattices; Filtration; Neural networksbusinessFiltrationNeural networksTRACE (psycholinguistics)Filtering; Insects; Lattices; Neuronsdescription
Sequence learning is a complex capability shown by living beings, able to extract information from the environment. Looking into the insect world, there are several examples where the presentation time of specific stimuli is considered to select the proper behavioural response. On the basis of previously developed neural models for sequence learning, inspired by the Drosophila melanogaster, a new formalization of key brain structures involved in the process is here provided. The input classification is performed through resonant neurons, stimulated by the complex dynamics generated in a lattice of recurrent spiking neurons modelling the Mushroom Bodies neuropile in the insect brain. The network devoted to the context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. Simulation results were reported to show the capabilities of the architecture.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-07-01 |