Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

Jürgensen, Anna-Maria; Sakagiannis, Panagiotis; Schleyer, Michael; Gerber, Bertram; Nawrot, Martin Paul

doi:10.1016/j.isci.2023.108640

Cited by 3 publications

(3 citation statements)

References 158 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two nonexclusive scenarios may be invoked. A reward prediction may arise from previously established context–reward associations ( Rescorla and Wagner 1972 ) or from the generalization of previously established cue–reward associations ( Jürgensen et al 2024 ). Our observation that unpaired training does not lead to odor avoidance when there is no possibility for context–reward associations to have been established ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Cognitive limits of larvalDrosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Sen,

El-Keredy,

Jacob

et al. 2024

Learn. Mem.

Self Cite

View full text Add to dashboard Cite

Drosophilalarvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larvalDrosophilafor their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning inDrosophilalarvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larvalDrosophila.

show abstract

Section: Discussionmentioning

confidence: 99%

Cognitive limits of larvalDrosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Sen,

El-Keredy,

Jacob

et al. 2024

Learn. Mem.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: A Return To Prediction Errormentioning

confidence: 99%

“…As presented, there is no mechanism to increase the strength of synapses, and instead it is explored how this network can produce behavioral extinction of memory by generating a second memory of opposing valence when the reinforcer is omitted. Juergensen et al (2024) present a similar model for larval learning, this time implemented in a spiking network, with only two MBONs that excite their own and inhibit their opposite DAN; a similar learning rule depresses KC-MBON weights for recently active KCs when a DAN is active, but in this case, depressed synapses are restored toward their initial state when MBONs spike. Zhao et al (2021) argue that simple associative learning in the form of a KC-MBON weight change proportional to a (temporal trace of) KC activity correlated with reinforcer strength cannot account for data in which different reinforcer strengths are delivered in different temporal relationships to odor (e.g., one large shock delivered at the start or end of odor presentation vs. multiple small shocks throughout).…”

Section: A Return To Prediction Errormentioning

confidence: 99%

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body

Webb

2024

Learn. Mem.

View full text Add to dashboard Cite

The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption—in line with top-down theories of associative learning—that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.

show abstract

A behavioral architecture for realistic simulations ofDrosophilalarva locomotion and foraging

Sakagiannis

Jürgensen

Nawrot

2021

Preprint

View full text Add to dashboard Cite

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and topdown modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Interindividual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

show abstract

Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

Cited by 3 publications

References 158 publications

Cognitive limits of larvalDrosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Cognitive limits of larvalDrosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body

A behavioral architecture for realistic simulations ofDrosophilalarva locomotion and foraging

Contact Info

Product

Resources

About