Biology subjects

Jürgensen, A.-M.

Publications and source records attributed to Jürgensen, A.-M..

4 recordsLinked to original sources

bioRxiv2026

Context makes the difference: Temporally Resolved Dopaminergic Teaching Signals Shape Associative Memory in Drosophila Larvae

Animals can adapt their behavioral responses to environmental cues by learning from experience. This ability relies on the formation and recall of memories that are shaped by beneficial or detrimental consequences and regulated by the dopaminergic system, which is highly conserved across insect species. In the Drosophila melanogaster larva, eight of total [~]120 dopaminergic neurons (DANs) innervate the mushroom body (MB), a key center for associative memory. This subset of DANs can be anatomically grouped into two clusters of four cells: the primary protocerebral anterior medial (pPAM) cluster, associated with reward signaling, and the dorsolateral 1 (DL1) cluster, associated with punishment. Such a functional dichotomy is observed in larval and adult Drosophila and reflects a fundamental organizational principle of reinforcement learning across invertebrate and even vertebrate species. Aversive reinforcement through high-salt exposure is encoded within the DL1 cluster in a combinatorial and heterogeneous manner, critically involving two neurons, DAN-f1 and DAN-g1. Using temporally precise optogenetic activation and inhibition during olfactory conditioning, we show that these neurons can modulate memory strength and valence. Their effects are most often consistent and predictable, enabling accurate computational modeling of DAN-driven teaching signals. By manipulating the intrinsic physiology of DAN-f1 and DAN-g1 and altering the valence of gustatory input, we are beginning to understand at the single-cell level how dopaminergic activity is systematically adjusted to control memory formation.

neuroscience↗

bioRxiv2023

Minimal circuit motifs for second-order conditioning in the insect mushroom body

In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. Once a sensory cue is established as a predictor, it can also serve as indirect reinforcement, a phenomenon referred to as second-order conditioning. In the insect mushroom body, such associations are encoded in the plasticity of the synapses between the intrinsic and output neurons of the mushroom body, a process mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explore minimal circuit motifs in the mushroom body for their ability to support second-order conditioning. We found that dopaminergic neurons can either be activated directly by the mushroom bodys intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrate that the circuit motifs differ in their computational efficiency and robustness and suggest a particular motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising additional candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

neuroscience↗

bioRxiv2021

A realistic locomotory model of Drosophila larva for behavioral simulations.

The Drosophila larva is extensively used as model organism in neuroethological studies where precise behavioral tracking enables the statistical analysis of individual and population-level behavioral metrics that can inform mathematical models of larval behavior. Here, we propose a hierarchical model architecture comprising three layers to facilitate modular model construction, closed-loop simulations, and direct comparisons between empirical and simulated data. At the motor layer, the autonomous locomotory model is capable of performing exploration. Based on novel kinematic analyses our model features intermittent forward crawling that is phasically coupled to lateral bending. At the second layer, navigation is achieved via active sensing in a simulated environment and top-down modulation of locomotion. At the top layer, behavioral adaptation entails associative learning. We evaluate virtual larval behavior across agent-based simulations of autonomous free exploration, chemotaxis, and odor preference testing. Our behavioral architecture is ideally suited for the modular combination of neuromechanical, neural or mere statistical model components, facilitating their evaluation, comparison, extension and integration into multifunctional control architectures.

neuroscience↗

bioRxiv2021

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

neuroscience↗