Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robots
Rodents are optimal real-world foragers that regulate internal states maintaining a dynamic stability with their surroundings. How these internal drive based behaviors are regulated remains unclear. Based on the physiological notion of allostasis, we investigate 377 378 M. Sanchez-Fibla et al. a minimal control system able to approximate their behavior. Allostasis is the process of achieving stability with the environment through change, opposed to homeostasis which achieves it through constancy. Following this principle, the so-called allostatic control system orchestrates the interaction of the homeostatic modules by changing their desired values in order to achieve stability. We use a minimal number of subsystems and estimate the model parameters from rat behavioral data in three experimental setups: free exploration, presence of reward, delivery of cues with reward predictive value. From this analysis, we show that a rat is influenced by the shape of the arena in terms of its openness. We then use the estimated model configurations to control a simulated and real robot which captures essential properties of the observed rat behavior. The allostatic reactive control model is proposed as an augmentation of the Distributed Adaptive Control architecture and provides a further contribution towards the realization of an artificial rodent.
In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.
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