Lateralised memory networks may explain the use of higher-order visual features in navigating insects

Salvato in:
Dettagli Bibliografici
Pubblicato in:PLoS Computational Biology vol. 21, no. 6 (Jun 2025), p. e1012670-e1012693
Autore principale: Filippi, Giulio
Altri autori: Knight, James, Philippides, Andrew, Graham, Paul
Pubblicazione:
Public Library of Science
Soggetti:
Accesso online:Citation/Abstract
Full Text
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!

MARC

LEADER 00000nab a2200000uu 4500
001 3270579433
003 UK-CbPIL
022 |a 1553-734X 
022 |a 1553-7358 
024 7 |a 10.1371/journal.pcbi.1012670  |2 doi 
035 |a 3270579433 
045 2 |b d20250601  |b d20250630 
084 |a 174831  |2 nlm 
100 1 |a Filippi, Giulio 
245 1 |a Lateralised memory networks may explain the use of higher-order visual features in navigating insects 
260 |b Public Library of Science  |c Jun 2025 
513 |a Journal Article 
520 3 |a Many insects use memories of their visual environment to adaptively drive spatial behaviours. In ants, visual memories are fundamental for navigation, whereby foragers follow long visually guided routes to foraging sites and return to the location of their nest. Whilst we understand the basic visual pathway to the memory centres (Optic Lobes to Mushroom Bodies) involved in the storage of visual information, it is still largely unknown what type of representation of visual scenes underpins view-based navigation in ants. Several experimental studies have suggested ants use “higher-order” visual information – that is features extracted across the whole extent of a visual scene – which raises the question as to how these features might be computed. One such experimental study showed that ants can use the proportion of a shape experienced left of their visual centre to learn and recapitulate a route, a feature referred to as “fractional position of mass” (FPM). In this work, we use a simple model constrained by the known neuroanatomy and information processing properties of the Mushroom Bodies to explore whether the apparent use of the FPM could be a resulting factor of the bilateral organisation of the insect brain, all the whilst assuming a simple “retinotopic” view representation. We demonstrate that such bilaterally organised memory models can implicitly encode the FPM learned during training. We find that balancing the “quality” of the memory match across left and right hemispheres allows a trained model to retrieve the FPM defined direction, even when the model is tested with novel shapes, as demonstrated by ants. The result is shown to be largely independent of model parameter values, therefore suggesting that some aspects of higher-order processing of a visual scene may be emergent from the structure of the neural circuits, rather than computed in discrete processing modules. 
653 |a Feature extraction 
653 |a Memory 
653 |a Navigation 
653 |a Computation 
653 |a Data processing 
653 |a Experiments 
653 |a Neural networks 
653 |a Retina 
653 |a Insects 
653 |a Brain architecture 
653 |a Information processing 
653 |a Visual pathways 
653 |a Learning 
653 |a Mushroom bodies 
653 |a Hemispheres 
653 |a Ants 
653 |a Visual stimuli 
653 |a Representations 
653 |a Order processing 
653 |a Environmental 
700 1 |a Knight, James 
700 1 |a Philippides, Andrew 
700 1 |a Graham, Paul 
773 0 |t PLoS Computational Biology  |g vol. 21, no. 6 (Jun 2025), p. e1012670-e1012693 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3270579433/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3270579433/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3270579433/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch