scholarly journals The incentive circuit: memory dynamics in the mushroom body of Drosophila melanogaster

2021 ◽  
Author(s):  
Evripidis Gkanias ◽  
Li Yan McCurdy ◽  
Michael N Nitabach ◽  
Barbara Webb
Keyword(s):  
Drosophila Melanogaster ◽  
Mushroom Body ◽  
Learning Rule ◽  
Detailed Examination ◽  
Mushroom Bodies ◽  
Short Term ◽  
Output Neurons ◽  
Memory Acquisition ◽  
Exploration Exploitation

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting acquisition, forgetting, and assimilation of memories. Drawing on this data, we identify a series of microcircuits within the mushroom bodies that reveal, for each motivational state, three different roles of dopaminergic and mushroom body output neurons in the memory dynamics. These microcircuits share components and form a unified system for rapid memory acquisition, transfer from short-term to long-term, and exploration/exploitation trade-off. We show that combined with a novel biologically plausible learning rule, a computational model of the full circuit reproduces the observed changes in the activity of each of these neurons in conditioning paradigms and can be used for flexible behavioural control.

scholarly journals Mushroom Body Ablation Impairs Short-Term Memory and Long-Term Memory of Courtship Conditioning in Drosophila melanogaster

Neuron ◽  
1999 ◽  
Vol 24 (4) ◽  
pp. 967-977 ◽  
Author(s):  
Sean M.J McBride ◽  
Giovanna Giuliani ◽  
Catherine Choi ◽  
Paul Krause ◽  
Dana Correale ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Mushroom Body ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Courtship Conditioning

scholarly journals Long-term memory requires sequential protein synthesis in three subsets of mushroom body output neurons in Drosophila

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jie-Kai Wu ◽  
Chu-Yi Tai ◽  
Kuan-Lin Feng ◽  
Shiu-Ling Chen ◽  
Chun-Chao Chen ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Mushroom Body ◽  
Long Term Memory ◽  
Term Memory ◽  
Output Neurons

scholarly journals Metabolic effects of CO 2 anaesthesia in Drosophila melanogaster

2012 ◽  
Vol 8 (6) ◽  
pp. 1050-1054 ◽  
Author(s):  
H. Colinet ◽  
D. Renault
Keyword(s):  
Drosophila Melanogaster ◽  
Recovery Period ◽  
Metabolic Changes ◽  
Metabolic Effects ◽  
Short Term ◽  
Short And Long Term ◽  
Metabolic Variation ◽  
Negative Side Effects ◽  
Entomological Research

Immobilization of insects is necessary for various experimental purposes, and CO 2 exposure remains the most popular anaesthetic method in entomological research. A number of negative side effects of CO 2 anaesthesia have been reported, but CO 2 probably brings about metabolic modifications that are poorly known. In this work, we used GC/MS-based metabolic fingerprinting to assess the effect of CO 2 anaesthesia in Drosophila melanogaster adults. We analysed metabolic variation of flies submitted to acute CO 2 exposure and assessed the temporal metabolic changes during short- and long-term recovery. We found that D. melanogaster metabotypes were significantly affected by the anaesthetic treatment. Metabolic changes caused by acute CO 2 exposure were still manifested after 14 h of recovery. However, we found no evidence of metabolic alterations when a long recovery period was allowed (more than 24 h). This study points to some metabolic pathways altered during CO 2 anaesthesia (e.g. energetic metabolism). Evidence of short-term metabolic changes indicates that CO 2 anaesthesia should be used with utmost caution in physiological studies when a short recovery is allowed. In spite of this, CO 2 treatment seems to be an acceptable anaesthetic method provided that a long recovery period is allowed (more than 24 h).

scholarly journals Drosophila ORB protein in two mushroom body output neurons is necessary for long-term memory formation

2013 ◽  
Vol 110 (19) ◽  
pp. 7898-7903 ◽  
Author(s):  
T.-P. Pai ◽  
C.-C. Chen ◽  
H.-H. Lin ◽  
A.-L. Chin ◽  
J. S.-Y. Lai ◽  
...  
Keyword(s):  
Mushroom Body ◽  
Memory Formation ◽  
Long Term Memory ◽  
Term Memory ◽  
Output Neurons

A Canonical Microfunction for Learning Perceptual Invariances

Perception ◽  
1996 ◽  
Vol 25 (2) ◽  
pp. 207-220 ◽  
Author(s):  
James V Stone
Keyword(s):  
Model Neuron ◽  
Learning Rule ◽  
Image Sequences ◽  
Weight Changes ◽  
Short Term ◽  
Stereo Disparity ◽  
Random Dot Stereograms ◽  
Long Time ◽  
Generic Strategy

An unsupervised method is presented which permits a set of model neurons, or a microcircuit, to learn low-level vision tasks, such as the extraction of surface depth. Each microcircuit implements a simple, generic strategy which is based on a key assumption: perceptually salient visual invariances, such as surface depth, vary smoothly over time. In the process of learning to extract smoothly varying invariances, each microcircuit maximises a microfunction. This is achieved by means of a learning rule which maximises the long-term variance of the state of a model neuron and simultaneously minimises its short-term variance. The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively. The method is demonstrated on a hyperacuity task: estimating subpixel stereo disparity from a temporal sequence of random-dot stereograms. After learning, the microcircuit generalises, without additional learning, to previously unseen image sequences. It is proposed that the approach adopted here may be used to define a canonical microfunction, which can be used to learn many perceptually salient invariances.

A pair of dopamine neurons mediate chronic stress signals to induce learning deficit in Drosophila melanogaster

2021 ◽  
Vol 118 (42) ◽  
pp. e2023674118
Author(s):  
Jia Jia ◽  
Lei He ◽  
Junfei Yang ◽  
Yichun Shuai ◽  
Jingjing Yang ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Chronic Stress ◽  
Dopaminergic Neurons ◽  
Mushroom Body ◽  
Dopamine Neurons ◽  
Learning Deficit ◽  
Dopaminergic Activity ◽  
Output Neurons ◽  
Stress Signals ◽  
Underlying Mechanisms

Chronic stress could induce severe cognitive impairments. Despite extensive investigations in mammalian models, the underlying mechanisms remain obscure. Here, we show that chronic stress could induce dramatic learning and memory deficits in Drosophila melanogaster. The chronic stress–induced learning deficit (CSLD) is long lasting and associated with other depression-like behaviors. We demonstrated that excessive dopaminergic activity provokes susceptibility to CSLD. Remarkably, a pair of PPL1-γ1pedc dopaminergic neurons that project to the mushroom body (MB) γ1pedc compartment play a key role in regulating susceptibility to CSLD so that stress-induced PPL1-γ1pedc hyperactivity facilitates the development of CSLD. Consistently, the mushroom body output neurons (MBON) of the γ1pedc compartment, MBON-γ1pedc>α/β neurons, are important for modulating susceptibility to CSLD. Imaging studies showed that dopaminergic activity is necessary to provoke the development of chronic stress–induced maladaptations in the MB network. Together, our data support that PPL1-γ1pedc mediates chronic stress signals to drive allostatic maladaptations in the MB network that lead to CSLD.

Mushroom body neuronal remodelling is necessary for short-term but not for long-term courtship memory in Drosophila

2012 ◽  
Vol 35 (11) ◽  
pp. 1684-1691 ◽  
Author(s):  
Christelle Redt-Clouet ◽  
Séverine Trannoy ◽  
Ana Boulanger ◽  
Elena Tokmatcheva ◽  
Elena Savvateeva-Popova ◽  
...  
Keyword(s):  
Mushroom Body ◽  
Short Term

Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints

1996 ◽  
Vol 8 (7) ◽  
pp. 1463-1492 ◽  
Author(s):  
James V. Stone
Keyword(s):  
Low Frequency ◽  
Learning Rule ◽  
Input Sequence ◽  
Image Sequences ◽  
Weight Changes ◽  
Short Term ◽  
Long Time ◽  
Temporal Sequences ◽  
Perceptual Systems

A model is presented for unsupervised learning of low level vision tasks, such as the extraction of surface depth. A key assumption is that perceptually salient visual parameters (e.g., surface depth) vary smoothly over time. This assumption is used to derive a learning rule that maximizes the long-term variance of each unit's outputs, whilst simultaneously minimizing its short-term variance. The length of the half-life associated with each of these variances is not critical to the success of the algorithm. The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively. This maximizes the information throughput with respect to low-frequency parameters implicit in the input sequence. The model is used to learn stereo disparity from temporal sequences of random-dot and gray-level stereograms containing synthetically generated subpixel disparities. The presence of temporal discontinuities in disparity does not prevent learning or generalization to previously unseen image sequences. The implications of this class of unsupervised methods for learning in perceptual systems are discussed.

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