Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila
Open accessAbstract
When animals learn, plasticity in brain networks that respond to specific cues results in a change in the behavior that these cues elicit. Individual network components in the mushroom bodies of the fruit fly Drosophila melanogaster represent cues, learning signals and behavioral outcomes of learned experience. Recent findings have highlighted the importance of dopamine-driven plasticity and activity in feedback and feedforward connections, between various elements of the mushroom body neural network. These computational motifs have been shown to be crucial for long term olfactory memory consolidation, integration of internal states, re-evaluation and updating of learned information. The often recurrent circuit anatomy and a prolonged requirement for activity in parts of these underlying networks, suggest that self-sustained and precisely timed activity is a fundamental feature of network computations in the insect brain. Together these processes allow flies to continuously adjust the content of their learned knowledge and direct their behavior in a way that best represents learned expectations and serves their most pressing current needs.
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The authors moved the simulation framework from PyBullet to MuJoCo, a more widely used physics simulator.
“Second, we moved the simulation framework from PyBullet to MuJoCo 45 , a more widely used physics simulator.”
From NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila (2024) · cites this paper as [45]
The Introduction and Conclusions discuss neural mechanisms of memory in Drosophila, not physics simulation. Searches for PyBullet, MuJoCo, and a change of simulation framework found no relevant passage in our copy. The source therefore does not verify the cited statement. (LLM, conf=0.78)
Experimental — accuracy not yet measuredFlyGym is implemented based on MuJoCo and the dm control library and complies with the Gymnasium API for MDP.
“FlyGym is implemented based on MuJoCo 45 and the dm control 99 library and complies with the Gymnasium API for MDP 12 .”
From NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila (2024) · cites this paper as [45]
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References 60 · Cited by 281
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- Authors
- Cognigni, P., Felsenberg, J., & Waddell, S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Waddell, Scott
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 49
- Pages
- 51-58
- ISSN
- 0959-4388 (ISSN-L)
- PMC
- PMC5981003
- OpenAlex
- W2774719659
- Cited by
- 281
- References
- 60
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 41 · 2023: 32 · 2022: 34 · 2021: 36 · 2020: 36 · 2019: 33 · 2018: 22
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Neurobiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (FE 1563/1-1)
- Gatsby Charitable Foundation and Bettencourt–Schueller Foundation
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- Drosophila melanogaster, mushroom body, olfactory memory, memory consolidation, memory updating, recurrent neural circuits, memory expression, neural network computation
- MeSH terms
- Animals, Brain, Computer Simulation, Drosophila, Learning, Models, Neurological, Neural Pathways, Neurons
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