Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila

Cognigni, Paola; Felsenberg, Johannes; Waddell, Scott

doi:10.1016/j.conb.2017.12.002

Cited by 234 publications

(259 citation statements)

References 60 publications

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“…Here we speculate that the aerial-terrestrial ecology exploited by insects is comparable to elaborate three-dimensional ecologies of reefs and escarpments exploited by stomatopod and caridean species. Making allowance for what is a miniscule sample size, our results suggest that highly mobile species occupying dynamic three-dimensional ecologies have retained the mushroom body ground pattern, which in insects provides continuous updating of spatial associations and their valences (Cognigni et al, 2018). That mushroom bodies are crucial for spatial awareness is supported by studies demonstrating that they increase in size as a response to acquiring information about threedimensional space (Kühn-Bühlmann and Wehner, 2006;Montgomery et al, 2016;van Dijk et al, 2017).…”

Section: Divergent Evolution Of the Pancrustacean Mushroom Body: Corrsupporting

confidence: 52%

“…As demonstrated in Drosophila melanogaster, paired mushroom bodies in the insect brain play manifold roles in learning and memory (Aso et al, 2014a, b;Cognigni et al, 2018). One diagnostic tool for identifying putative mushroom body homologues in other mandibulates is an antibody raised against the catalytic subunit of protein kinase A, encoded by the Drosophila gene DC0 (Kalderon and Rubin, 1988), and required for effective learning and memory (Skoulakis et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…In stomatopods, longitudinal divisions are represented by four columnar lobes extending together approximately in parallel (Wolff et al, 2017). In Drosophila and other insects these characteristic attributes have been shown to be critical in supporting functions relating to the modality, valence, stored memories and behavioral relevance of information computed by the mushroom body (Li and Strausfeld, 1999;Aso et al, 2014a,b;Owald et al, 2015;Takemura et al, 2017;Hattori et al, 2017;Cognigni et al, 2018). In total, thirteen characters have been identified that define both the insect (Drosophila) and mantis shrimp (Stomatopod) mushroom bodies (see, Wolff et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Mushroom Body Homology and Divergence across Pancrustacea

Strausfeld¹,

Wolff

Sayre

2019

Preprint

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Descriptions of crustacean brains have mainly focused on three highly derived lineages: the reptantian infraorders represented by spiny lobsters, lobsters, and crayfish. Those descriptions advocate the view that dome-or cap-like neuropils, referred to as "hemiellipsoid bodies," are the ground pattern organization of centers that are comparable to insect mushroom bodies in processing olfactory information. Here we challenge the doctrine that hemiellipsoid bodies are a derived trait of crustaceans, whereas mushroom bodies are a derived trait of hexapods. We demonstrate that mushroom bodies typify lineages that arose before Reptantia and exist in Reptantia. We show that evolved variations of the mushroom body ground pattern are, in some lineages, defined by extreme diminution or loss and, in others, by the incorporation of mushroom body circuits into lobeless centers. Such transformations are ascribed to modifications of the columnar organization of mushroom body lobes that, as shown in Drosophila and other hexapods, contain networks essential for learning and memory. We propose that lobed mushroom bodies distinguish crustaceans that negotiate the multidimensionality of complex ecologies, where continuous updating of multistimulus valence and memory is paramount.

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Section: Divergent Evolution Of the Pancrustacean Mushroom Body: Corrsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mushroom Body Homology and Divergence across Pancrustacea

Strausfeld¹,

Wolff

Sayre

2019

Preprint

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“…Lastly, our work here addresses memory acquisition, but not memory consolidation, which is supported by distinct circuits within the MB (43). Incorporating memory stabilising mechanisms may help to better align our simulations of genetic interventions with fly behaviour in conditioning experiments.…”

Section: Model Limitationsmentioning

confidence: 99%

Learning with reward prediction errors in a model of the Drosophila mushroom body

Bennett

Philippides

Nowotny

2019

Preprint

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Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body (MB), where dopamine neurons (DANs) signal reinforcing stimuli to modulate plasticity presynaptic to MB output neurons (MBONs). Here, we extend previous MB models, in which DANs signal absolute rewards, proposing instead that DANs signal reward prediction errors (RPEs) by utilising feedback reward predictions from MBONs. We formulate plasticity rules that minimise RPEs, and use simulations to verify that MBONs learn accurate reward predictions. We postulate as yet unobserved connectivity, which not only overcomes limitations in the experimentally constrained model, but also explains additional experimental observations that connect MB physiology to learning. The original, experimentally constrained model and the augmented model capture a broad range of established fly behaviours, and together make five predictions that can be tested using established experimental methods.

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“…For example, memories can be consolidated into a persistent form and maintained 5,6,8,15−17 , extinguished 5, 7,9,11,14,18 , or expanded and combined into chains of associations (as in higher-order conditioning 7,19,20 ). Furthermore, learning can itself be flexible and depends both on present context and past history 14,21,22 . These are fundamental brain functions across the animal kingdom, but the learning algorithms used by brains and their circuit implementations are still unclear.…”

Section: Introductionmentioning

confidence: 99%

Multilevel feedback architecture for adaptive regulation of learning in the insect brain

Eschbach

Fushiki

Winding

et al. 2019

Preprint

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Modulatory (e.g. dopaminergic) neurons provide "teaching signals" that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of the Drosophila larva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one-and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

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Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila

Cited by 234 publications

References 60 publications

Mushroom Body Homology and Divergence across Pancrustacea

Mushroom Body Homology and Divergence across Pancrustacea

Learning with reward prediction errors in a model of the Drosophila mushroom body

Multilevel feedback architecture for adaptive regulation of learning in the insect brain

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