Barbara Webb

A Model of Non-elemental Associative Learning in the Mushroom Body Neuropil of the Insect Brain

We developed a computational model of the mushroom body (MB), a prominent region of multimodal integration in the insect brain, and tested the models performance for non-elemental associative learning in visual pattern avoidance tasks. We employ a realistic spiking neuron model and spike time dependent plasticity, and learning performance is investigated in closed-loop conditions. We show that the distinctive neuroarchitecture (divergence onto MB neurons and convergence from MB neurons, with an otherwise non-specific connectivity) is sufficient for solving non-elemental learning tasks and thus modulating underlying reflexes in context-dependent, heterarchical manner.

Using robots to model animals: a cricket test

Abstract The sensorimotor problems faced by animals and by robots have much in common, yet identifying this similarity has so far led to very few successful attempts to implement specific biological neural control structures on robots. One major limitation is that understanding of the biological mechanisms is insufficient for straightforward installation. However, using robots as a method of physically modeling animal systems can potentially contribute to our understanding of these mechanisms. This approach is employed in an investigation of phonotaxis (sound-seeking) in crickets. The process of building a robot forms the basis of a critical evaluation of the neuroethological evidence about the cricket, and generates an alternative hypothesis to explain this evidence. The explanatory power of this hypothesis is explored by testing and analyzing the behaviour of the robot that embodies it. The robot behaved like the cricket, competently and robustly finding its way to a specific sound source under a variety of conditions. It is argued that the methodology is more appropriate than symbolic simulation for the kinds of problems raised in the investigation of sensorimotor behaviour in animals and robots.

Robots in invertebrate neuroscience

Can we now build artificial animals? A combination of robot technology and neuroethological knowledge is enabling the development of realistic physical models of biological systems. And such systems are not only of interest to engineers. By exploring identified neural control circuits in the appropriate functional and environmental context, new insights are also provided to biologists.

Robot phonotaxis in the wild : a biologically inspired approach to outdoor sound localization

Cricket phonotaxis (sound localization behavior) was implemented on an autonomous outdoor robot platform inspired by cockroach locomotion. This required the integration of a novel robot morphology (Whegs) with a biologically based auditory processing circuit and neural control system, as well as interfacing this to a new tracking device and software architecture for running robot experiments. In repeated tests, the robot is shown to be capable of tracking towards a simulated male cricket song over natural terrain. Range fractionation and gain control were added to the auditory control circuit in order to deal with the substantial change in amplitude of the signal as the robot approached the outdoor sound stimulus. We also discuss issues related to acoustic interference from motor noise, the need for a motor feedback mechanism to better regulate the drive signal and plans for future work incorporating additional sensory systems on this platform.

New neural circuits for robot phonotaxis

W. Grey Walter built robotic systems to improve understanding of biological systems. In that tradition, this paper reports ongoing work on a robot model of cricket sound localization. The main advances are the inclusion of a much larger range of neuroethological detail, and the investigation of multimodal influences on the behaviour. The former allows exploration of the functionality of identified neurons in the insect, including the possible roles of multiple sensory fibres, mutually inhibitory connections, and brain neurons with pattern-filtering properties. The latter focuses on the inclusion of an optomotor stabilization response, and how this might improve tracking, particularly under conditions of random disturbance.

Animals Versus Animats: Or Why Not Model the Real Iguana?

The overlapping fields of adaptive behavior and artificial life are often described as novel approaches to biology. They focus attention on bottom-up explanations and how lifelike phenomena can result from relatively simple systems interacting dynamically with their environments. They are also characterized by the use of synthetic methodologies, that is, building artificial systems as a means of exploring these ideas. Two differing approaches can be distinguished: building models of specific animal systems and assessing them within complete behavior—environment loops; and exploring the behavior of invented artificial animals, often called animats, under similar conditions. An obvious question about the latter approach is, how can we learn about real biology from simulation of non-existent animals? In this article I will argue, first, that animat research, to the extent that it is relevant to biology, should also be considered as model building. Animat simulations do, implicitly, represent hypotheses about, and should be evaluated by comparison to, animals. Casting this research in terms of invented agents serves only to limit the ability to draw useful conclusions from it by deflecting or deferring any serious comparisons of the model mechanisms and results with real biological systems. Claims that animat models are meant to be existence proofs, idealizations, or represent general problems in biology do not make these models qualitatively different from more conventional models of specific animals, nor undermine the ultimate requirement to justify this work by making concrete comparisons with empirical data. It is thus suggested that we will learn more by choosing real, and not made-up, targets for our models.

New technologies for testing a model of cricket phonotaxis on an outdoor robot

If biological inspiration can be used to build robots that deal robustly with complex environments, it should be possible to demonstrate that ‘biorobots’ can function in natural environments. We report on initial outdoor experiments with a robot designed to emulate cricket behaviour. The work integrates a detailed neural model of auditory localisation in the cricket with a robot morphology that incorporates principles of six-legged locomotion. We demonstrate that it can successfully track a cricket calling song over natural terrain. Limitations in its capability are evaluated, and a number of biologically based improvements are suggested for future work.

Physical and temporal scaling considerations in a robot model of cricket calling song preference

Behavioral experiments with crickets show that female crickets respond to male calling songs with syllable rates within a certain bandwidth only. We have made a robot model in which we implement a simple neural controller that is less complex than the controllers traditionally hypothesized for cricket phonotaxis and syllable rate preference. The simple controller, which had been successfully used with a slowed and simplified signal, is here demonstrated to function, using songs with identical parameters to those found in real male cricket song, using an analog electronic model of the peripheral auditory morphology of the female cricket as the sensor. We put the robot under the same experimental conditions as the female crickets, and it responds with phonotaxis to calling songs of real male Gryllus bimaculatus. Further, the robot only responds to songs with syllable rates within a bandwidth similar to the bandwidth found for crickets. By making polar plots of the heading direction of the robot, we obtain behavioral data that can be used in statistical analyses. These analyses show that there are statistically significant differences between the behavioral responses to calling songs with syllable rates within the bandwidth and calling songs with syllable rates outside the bandwidth. This gives the verification that the simple neural control mechanism (together with morphological auditory matched filtering) can account for the syllable rate preference found in female crickets. With our robot system, we can now systematically explore the mechanisms controlling recognition and choice behavior in the female cricket by experimental replication.

Entropy-based visual homing

Visual homing allows a robot to navigate towards a previously visited goal relying on visual input. In this study, the robot homes by optimising the difference surface produced from the mutual image information between a snapshot image taken at the goal and the current image. This study proposes a new homing method with automatic gain tuning inspired by entropy distance. We also present experimental results using omni-directional image sets, which demonstrate not only the feasibility of the proposed idea for visual homing but also adaptive and robust homing performance for both static and dynamic environments.

Evolving a Neural Model of Insect Path Integration

Path integration is an important navigation strategy in many animal species. We use a genetic algorithm to evolve a novel neural model of path integration, based on input from cells that encode the heading of the agent in a manner comparable to the polarization-sensitive interneurons found in insects. The home vector is encoded as a population code across a circular array of cells that integrate this input. This code can be used to control return to the home position. We demonstrate the capabilities of the network under noisy conditions in simulation and on a robot.