Biology subjects

Giurfa, M.

Publications and source records attributed to Giurfa, M..

4 recordsLinked to original sources

bioRxiv2025

Statistical Olfactory Learning in Honey Bees

Statistical learning is a key mechanism for detecting regularities in sensory inputs1,2. Among its functions is the ability to extract regularities from sequences (of sounds, objects, odors, etc.)3, enabling species to predict future events and guide behavior. This capacity has been demonstrated in vertebrates2, including human infants4, non-human primates5, and birds6. However, the minimum computational architecture required for statistical learning remains unclear. To address this issue, we studied statistical learning in the honey bee (Apis mellifera), an invertebrate model for learning studies7. We show that bees learn and recall the temporal structure of sequences of odorants, suggesting that statistical learning is a fundamental component of a conserved cognitive toolkit present even in invertebrates.

neuroscience↗

bioRxiv2023

Smell and Aftersmell: Fast Calcium Imaging Dynamics of Honey Bee Olfactory Coding

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus- specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning- induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

neuroscience↗

bioRxiv2021

Formic acid modulates latency and accuracy of nestmate recognition in carpenter ants

Decision-making processes face the dilemma of being accurate or faster, a phenomenon that has been described as speed-accuracy trade-off (SAT) in numerous studies on animal behaviour. In social insects, discriminating between colony members and aliens is subjected to this trade-off as rapid and accurate rejection of enemies is of primary importance for the maintenance and ecological success of insect societies. Recognition cues distinguishing aliens from nestmates are embedded in the cuticular hydrocarbon (CHC) layer and vary among colonies. In walking carpenter ants, exposure to formic acid (FA), an alarm pheromone, improves accuracy of nestmate recognition by decreasing both alien acceptance and nestmate rejection. Here we studied the effect of FA exposure on the spontaneous aggressive mandible opening response of harnessed Camponotus aethiops ants presented with either nestmate or alien CHCs. FA modulated both MOR accuracy and the latency to respond to odours of conspecifics. In particular, FA decreased MOR towards nestmates but increased it towards aliens. Furthermore, FA decreased MOR latency towards aliens but not towards nestmates. As response latency can be used as a proxy of response speed, we conclude that contrary to the prediction of the SAT theory, ants did not trade off speed against accuracy in the process of nestmate recognition. Summary statementExposure to an alarm pheromone increases both latency and accuracy of the response to recognition cues in ants

animal behavior and cognition↗

bioRxiv2018

Higher-order discrimination learning by honey bees in a virtual environment

Non-elemental learning constitutes a cognitive challenge because, contrary to elemental learning forms, it does not rely on simple associations, as events to be learned are usually ambiguous in terms of reinforcement outcome. Negative patterning constitutes a paradigmatic case of non-elemental learning, as subjects have to learn that single elements A and B are reinforced while their conjunctive representation AB is not reinforced (A+, B+ vs. AB-). Solving this problem requires treating the compound AB as being different from the linear sum of its components in order to overcome stimulus ambiguity (A+/A- and B+/B-). The honey bee is the only insect capable of mastering negative patterning as shown by numerous studies restricted mainly to the olfactory domain. Here we studied the capacity of bees to solve a negative patterning discrimination in the visual domain and used to this end a virtual reality (VR) environment in which a tethered bee walking stationary on a treadmill faces visual stimuli projected on a semicircular screen. Stimuli are updated by the bees movements, thus creating an immersive environment. Bees were trained to discriminate single-colored gratings rewarded with sucrose solution (blue, green; A+, B+) from a non-rewarded composite grating (blue-green, AB-). Bees learned this discrimination in the VR environment and inhibited to this end linear processing of the composite grating, which otherwise is treated as the sum of its components. Our results show for the first time mastering of a non-linear visual discrimination in a VR environment by honey bees, thus highlighting the value of VR for the study of cognition in insects.

animal behavior and cognition↗