Multisensory navigation and neuronal plasticity in desert ants

Rößler, Wolfgang

doi:10.1016/j.tins.2023.03.008

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The mushroom body (MB) is a crucial brain structure for learning and encoding relationships between sensory cues and reinforcement in Drosophila ( De Belle and Heisenberg, 1994 ; Heisenberg, 1998 ; Zars, 2000 ; Heisenberg, 2003 ) and other insects ( Mizunami, Weibrecht, and Strausfeld, 1998 ; Zars, 2000 ; Menzel and Giurfa, 2001 ; Rössler, 2023 ) alike. Its intrinsic neurons (Kenyon cells, KCs) have been shown to encode the identity of sensory inputs in Drosophila ( Wang et al, 2004 ; Turner, Bazhenov, and Laurent, 2008 ; Honegger, Campbell, and Turner, 2011 ; Lin et al, 2014 ) and other insects ( Perez-Orive et al, 2002 ; Stopfer, Jayaraman, and Laurent, 2003 ; Szyszka et al, 2005 ; Demmer and Kloppenburg, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

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

Jürgensen,

Schmitt,

Nawrot

2024

Front. Physiol.

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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. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is 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 explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body’s intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

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Section: Introductionmentioning

confidence: 99%

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

Jürgensen,

Schmitt,

Nawrot

2024

Front. Physiol.

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“…PI in ants includes the ability to return home from a novel location by keeping track of their own outgoing movements. PI has two main requirements, a mechanism to keep track of speed/distance travelled such as odometry ( Wittlinger et al, 2007 ; Wolf, 2011 ) and a mechanism to keep track of orientation using information derived from environmental compass cues and neuronal systems that track rotations ( Wehner, 1976 ; Rössler, 2023 ). PI allows for ants to explore unfamiliar areas and return on direct paths to their nest when they have food.…”

Section: Introductionmentioning

confidence: 99%

Investigating visual navigation using spiking neural network models of the insect mushroom bodies

Jesusanmi,

Amin,

Domcsek

et al. 2024

Front. Physiol.

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Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN–a library for building GPU accelerated spiking neural networks–we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model’s Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

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“…In Drosophila [12][13][14][15] and other insects [15][16][17][18], the mushroom body (MB) is a crucial brain structure for learning and encoding relationships between sensory cues and reinforcement. The Kenyon cells (KC) are the intrinsic neurons of the MB, encoding the identity of sensory inputs in Drosophila [19][20][21] as well in other insects [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

Jürgensen,

Schmitt,

Nawrot

2023

Preprint

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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 body’s 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.

show abstract

Multisensory navigation and neuronal plasticity in desert ants

Cited by 9 publications

References 11 publications

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

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

Investigating visual navigation using spiking neural network models of the insect mushroom bodies

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

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