A Conserved Network for Control of Arthropod Exteroceptive Optical Flow Reflexes during Locomotion

Blustein, Daniel; Ayers, Joseph

doi:10.1007/978-3-642-15193-4_7

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Scientists often publish proposed neural networks explaining specific behaviors and it is easiest to select one of these. Simple taxes and kineses are well suited to this biorobotic approach 22 .…”

Section: Discussionmentioning

confidence: 99%

Designing and Implementing Nervous System Simulations on LEGO Robots

Blustein

Rosenthal

Ayers³

2013

JoVE

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We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step of the method is to develop a nervous system simulation of specific reflexive behaviors of an appropriate model organism; here we use the American Lobster. Exteroceptive reflexes mediated by decussating (crossing) neural connections can explain an animal's taxis towards or away from a stimulus as described by Braitenberg and are particularly well suited for investigation using the NXT platform.1 The nervous system simulation is programmed using LabVIEW software on the LEGO Mindstorms platform. Once the nervous system is tuned properly, behavioral experiments are run on the robot and on the animal under identical environmental conditions. By controlling the sensory milieu experienced by the specimens, differences in behavioral outputs can be observed. These differences may point to specific deficiencies in the nervous system model and serve to inform the iteration of the model for the particular behavior under study. This method allows for the experimental manipulation of electronic nervous systems and serves as a way to explore neuroscience hypotheses specifically regarding the neurophysiological basis of simple innate reflexive behaviors. The LEGO Mindstorms NXT kit provides an affordable and efficient platform on which to test preliminary biomimetic robot control schemes. The approach is also well suited for the high school classroom to serve as the foundation for a hands-on inquiry-based biorobotics curriculum. Video LinkThe video component of this article can be found at

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Section: Discussionmentioning

confidence: 99%

Designing and Implementing Nervous System Simulations on LEGO Robots

Blustein

Rosenthal

Ayers³

2013

JoVE

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“…This work also shows that the motion of the vehicle 2b is equivalent to a particle in a potential well and, therefore, it can display chaotic behaviour. Finally, a vision based implementation of Braitenberg vehicles 2a and 2b was presented in [13]. The goal of this work was to imitate several reflex responses of arthropods using optical flow as a sensory input.…”

Section: Introductionmentioning

confidence: 99%

Noise, morphology and control. An analysis of the stochastic behaviour of Braitenberg vehicles

Rañó

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Braitenberg vehicles are simple models of the motion of animal towards, or away from, a stimulus. They have been used to implement target reaching and avoidance behaviours in robotics, based, among others, on sound, light, distance, and pressure sensors. When the sensors are accurate enough-when they provide a high signal to noise ratiovariations on the readings can be neglected. Their behaviour, in these applications, can be explained using a non-linear deterministic dynamical system. For noisy sensors, or when the physical interaction between the robot and the measured variable is complex, a deterministic model is not good enough. Some examples include robots with cheap sensors, sensor prototypes, or settings where the interaction with the environment changes the measurements, like odour tracking-as the motion of the robot creates turbulences in the air. This paper presents the first analysis of the behaviour of Braitenberg vehicle 3a with noisy sensors. The mathematical equations of the evolution of the vehicle state probability are derived under white noise assumptions, and a bound for the vehicle state uncertainty is obtained assuming Gaussian probability distributions of the pose. These equations relate the morphological parameters of the vehicle, the noise variance and the function connecting the sensors to the actuators. The non-linear controller simulations illustrate the local validity of the model, and global simulations show how the vehicles converge.

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“…As a result, they have evolved a behavioral set that includes a broad variety of compensatory responses to perturbation. This behavioral set results from layered exteroceptive reflexes responding to exteroceptive sensor input resulting from changes in orientation relative to gravity, impediment, chemical cues, and hydrodynamic and optical flow (Ayers, 2004;Blustein & Ayers, 2010). These layered exteroceptive reflexes can form taxic responses to point sources of sound or chemicals (Westphal et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Conserved Neural Circuit-Based Architecture for Ambulatory and Undulatory Biomimetic Robots

Ayers¹,

Westphal²,

Blustein³

2011

mar technol soc j

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The adaptive capabilities of underwater organisms result from layered exteroceptive reflexes responding to gravity, impediment, and hydrodynamic and optical flow. In combination with taxic responses to point sources of sound or chemicals, these reflexes allow reactive autonomy in the most challenging of environments. We are developing a new generation of lobster and lamprey-based robots that operate under control by synaptic networks rather than algorithms. The networks, based on the command neuron, coordinating neuron, and central pattern generator architecture, code sensor input as labeled lines and activate shape memory alloy-based artificial muscles through a simple interface that couples excitation to contraction. We have completed the lamprey-based robot and are adapting this sensor, board, and actuator architecture to a new generation of the lobster-based robot. The networks are constructed from discrete time map-based neurons and synapses and are instantiated on the digital signal processing chip. A sensor board integrates inputs from a short baseline sonar array (for beacon tracking and supervisory control), accelerometer, a compass, antennae, and optionally chemosensors. Actuator control is mediated by pulse-width duty cycle coding generated by the electronic motor neurons and a comparator and power field-effect transistor (FET) system housed on low- and high-current driver boards. These circular boards are stacked in a tubular hull with the processor and batteries. This system can readily mimic the biomechanics of the model organisms by the addition of hydrodynamic control surfaces. The behavioral set results from chaining sequences of exteroceptive reflexes released by sensory feedback from the environment.

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A Conserved Network for Control of Arthropod Exteroceptive Optical Flow Reflexes during Locomotion

Cited by 5 publications

References 36 publications

Designing and Implementing Nervous System Simulations on LEGO Robots

Designing and Implementing Nervous System Simulations on LEGO Robots

Noise, morphology and control. An analysis of the stochastic behaviour of Braitenberg vehicles

A Conserved Neural Circuit-Based Architecture for Ambulatory and Undulatory Biomimetic Robots

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