Back

Frequency modulation of organelle calcium-dependent voltage oscillations: an essential memory trace produced by operant conditioning in Aplysia

Puygrenier, L.; Simmers, J.; Nargeot, R.

2026-08-04 neuroscience
10.64898/2026.07.30.741715 bioRxiv
Show abstract

Spontaneous voltage oscillations in neuronal ensembles play a critical role in memory formation and storage. Although oscillatory activities arising endogenously within central pattern-generating (CPG) networks underlie many rhythmic motor behaviors, the contribution of such autonomous signals to motor learning and memory remains poorly understood. Previously, we found that the buccal CPG network driving food-seeking behavior in Aplysia contains a subset of electrically-coupled neurons that produces spontaneous, variable-amplitude voltage oscillations instigating infrequent and irregular cycles of patterned motor output. This pattern-initiating activity originates from organelle-derived, inositol triphosphate (IP3) receptor-dependent calcium oscillations in a pair of identified decision-making neurons (B63) within the CPG subset (Bedecarrats et al., 2021). Here, we show that the cycle frequency of this spontaneous pacemaker mechanism based on intracellular calcium store release is persistently increased by operant reward-learning, and in association with increased B63 excitability, constitutes a fundamental memory trace for accelerated and stereotyped rhythmic food-seeking movements. eLife AssessmentSlow voltage oscillations in neuronal ensembles can contribute to learning and memory. This study identifies long-lasting plasticity in the dynamics of oscillatory intracellular calcium store release in key neurons of Aplysias food-seeking network, serving as a sub-cellular substrate for operant-reward learning that leads to the rhythmic expression of motor output producing compulsive-like behavior.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.