Browsing by Author "Mathews, Zenon"
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- A high-throughput behavioral paradigm for Drosophila olfaction - The FlywalkPublication . Steck, Kathrin; Veit, Daniel; Grandy, Ronald; Bermúdez i Badia, Sergi; Mathews, Zenon; Verschure, Paul; Hansson, Bill S.; Knaden, MarkusHow can odor-guided behavior of numerous individual Drosophila be assessed automatically with high temporal resolution? For this purpose we introduce the automatic integrated tracking and odor-delivery system Flywalk. In fifteen aligned small wind tunnels individual flies are exposed to repeated odor pulses, well defined in concentration and timing. The flies' positions are visually tracked, which allows quantification of the odor-evoked walking behavior with high temporal resolution of up to 100 ms. As a demonstration of Flywalk we show that the flies' behavior is odorant-specific; attractive odors elicit directed upwind movements, while repellent odors evoke decreased activity, followed by downwind movements. These changes in behavior differ between sexes. Furthermore our findings show that flies can evaluate the sex of a conspecific and males can determine a female's mating status based on olfactory cues. Consequently, Flywalk allows automatic screening of individual flies for their olfactory preference and sensitivity.
- PASAR: an integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systemsPublication . Mathews, Zenon; Bermúdez i Badia, Sergi; Verschureab, Paul F.M.JA wide range of neuroscientific studies suggest the existence of cognitive mechanisms like attention, prediction, anticipation and strong vertical interactions between different hierarchical layers of the brain while performing complex tasks. Despite advances in both cognitive brain research and in the development of brain-inspired artificial cognitive systems, the interplay of these key ingredients of cognition remain largely elusive and unquantified in complex real-world tasks. Furthermore, it has not yet been demonstrated how a self-contained hierarchical cognitive system acting under limited resource constraints can quantifiably benefit from the incorporation of top–down and bottom–up attentional mechanisms. In this context, an open fundamental question is how a data association mechanism can integrate bottom–up sensory information and top–down knowledge. Here, building on the Distributed Adaptive Control (DAC) architecture, we propose a single framework for integrating these different components of cognition and demonstrate the framework’s performance in solving real-world and simulated robot tasks. Using the model we quantify the interactions between prediction, anticipation, attention and memory. Our results support the strength of a complete system that incorporates attention, prediction and anticipation mechanisms compared to incomplete systems for real-world and complex tasks. We unveil the relevance of transient memory that underlines the utility of the above mechanisms for intelligent knowledge management in artificial sensorimotor systems. These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.
- The synthetic moth: a neuromorphic approach toward artificial olfaction in robotsPublication . Vouloutsi, Vasiliki; Lopez- Serrano, Lucas L.; Mathews, Zenon; Chimeno, Alex Escuredo; Ziyatdinov, Andrey; Perera i Lluna, Alexandre; Bermúdez i Badia, Sergi; Verschure, Paul F. M. J.