Hebbian learning generates individuality in spatial decision-making
Mohammad Salahshour, Iain D. Couzin
This study shows that Hebbian learning in neural networks can generate individual spatial preferences during decision-making tasks.
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University of KonstanzORCID 0000-0001-8556-4558Unclaimed ORCID profile · follow to get notified of new preprints
5 papers
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Mohammad Salahshour, Iain D. Couzin
This study shows that Hebbian learning in neural networks can generate individual spatial preferences during decision-making tasks.
This study introduces a semi-folded arena to observe collective trail formation in whole termite colonies under controlled conditions, showing early trail structures in most colonies within 6 minutes.
Peter Skovorodnikov, Jacopo Razzauti, Blair R. Costelloe…·Leslie B. Vosshall
FERAL is a video-understanding system that maps raw video directly to behavioral labels, outperforming existing methods across species and conditions with less training data.
Mohammad Salahshour, Iain D. Couzin
This paper introduces an allocentric framework for modeling collective motion, showing that coherent group behavior can emerge from navigational circuits without explicit alignment rules.
Lior Lebovich, Tom Alisch, Edward S. Redhead…·Benjamin L. de Bivort
This study examines spatiotemporal dynamics of locomotor decisions in Drosophila and humans, showing that future choices can be predicted through sustained locomotor signatures and highlighting the role of sensory and dopaminergic signaling.