ePoster

MELODIC MEMORIES: EXAMINING REPRESENTATIONAL DRIFT OF ZEBRA FINCH AUDITORY NEURONS IN A NOVEL INCREMENTAL LEARNING TASK

Massimiliano Cosciaand 4 co-authors

University of Groningen

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS02-07PM-072

Presentation

Date TBA

Board: PS02-07PM-072

Poster preview

MELODIC MEMORIES: EXAMINING REPRESENTATIONAL DRIFT OF ZEBRA FINCH AUDITORY NEURONS IN A NOVEL INCREMENTAL LEARNING TASK poster preview

Event Information

Poster Board

PS02-07PM-072

Abstract

Our brain constantly processes and assimilates new experiences across lifetime, yet how it preserves previously learned information while acquiring new stimuli remains unclear. New information may either be integrated independently from existing memory representations, or alternatively, the formation of new memories could reshape representations of older memories. Recently it was shown that some memory representations are not fully stable but undergo representational drift, a process thought to reflect plasticity mechanisms supporting the maintenance and reorganization of learned representations. To investigate whether novel learning experiences influence the rate of drift of pre-existing memory representations, we conducted a longitudinal single-photon calcium imaging study in the nidopallium caudomediale, a secondary auditory area of the zebra finch brain involved in storing and maintaining auditory vocal memories. We chose this songbird model because zebra finches have a behaviorally relevant ability to encode numerous memories of conspecific songs. We first repeatedly presented playbacks of a single conspecific song and tracked neuronal ensembles across days to assess stability of the representation. Subsequently we introduced additional conspecific song stimuli one by one. This experimental design allowed us to quantify the stability and drift of conspecific-responsive neuronal ensembles, and to dissociate time-dependent changes in memory representations from experience-dependent reorganization, providing insight into how continual learning impacts representational drift.

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