Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Shaikh, Danish; Rañó, Iñaki

doi:10.3389/fbioe.2020.565963

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…The braitenberg vehicle can alter how its sensors and actuators are connected to produce different target tracking behaviors [7]. For instance, if the connection between the sensor and the actuator is set up to be proportional, the actuator will respond to the stimulus in line with the stimulus when the sensor detects it, leading to the behavior known as object following [8]. It can enable object tracking by identifying the location and motion of the object if the braitenberg vehicle's sensors are configured as a camera or a sensing device like the Li DAR.…”

Section: Braitenberg Vehicle Principlementioning

confidence: 99%

An improved braitenberg policy for object tracking

2024

ACE

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In vision-based unmanned vehicle tracking of moving vehicles, it is difficult to achieve accurate tracking of motorized vehicles with conventional fixed-parameter PID controllers due to their varying speeds and changes over time. To generate control signals for the systems and move the controlled object, the PID controller performs calculations based on percentage, integral, and derivative data. This is the discrete PID controller method. In this paper an improved tracking method, the Braitenberg policy, is proposed and a series of experiments are conducted in Simulink. The experimental results verify that the method can achieve stable tracking in simple environment for both general and extreme cases. The method can be used for future experiments for both general and extreme cases. The method can be used for future experiments in more scenarios. Braitenberg policy provides stronger and more stable tracking performance in extreme cases compared to conventional PID control. The algorithm provides more opportunities for tracking research and a small idea for the integration of multiple algorithms.

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Section: Braitenberg Vehicle Principlementioning

confidence: 99%

An improved braitenberg policy for object tracking

2024

ACE

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“…Tetrapods and bats use oscillatory auditory cues to maintain thermal stability and avoid obstacles, respectively. [90] Owing to its biocompatibility and applicability for ultrasound imaging, ultrasound is a promising stimulus for actuation and locomotion. The standing waves generated by propagating an ultrasonic field are used for controlling and manipulating synthetic agents, [91,92] similar to the echolocation by animals.…”

Section: Mechanical Taxesmentioning

confidence: 99%

Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales

Pumera

et al. 2022

Advanced Materials

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Figure 1. Cognate collective taxis behaviors realized by organisms and robots across scales. A) Collective migration of particle robots and cells. Loosely coupled particles present collective phototaxis (left). Reproduced with permission. [11] Copyright 2019, Springer Nature. Cells perform collective migration in 3D environments (right). Reproduced with permission. [4] Copyright 2018, Springer Nature. B) Self-assembly of robots and bacteria. A swarm of 1024 kilobots form a star-like pattern (left). Reproduced with permission. [14] Copyright 2014, AAAS. A swarm of E. coli cells form a right-handed colony (right). Scale bar: 5 mm. Reproduced with permission. [15] Copyright 2011, The Proceedings of the National Academy of Sciences. C) Collective taxes of macroscopic organisms (e.g., fishes, birds, and ants) and robots. D) Collective taxes of microorganisms (e.g., sperms, [19] cells, and bacteria [20] ) and microrobots. [21] E) Connection constructed by ants and NPs. Weaver ants establish a bridge to connect leaves (left). Reproduced with permission. [7] Copyright 2002, Springer Nature. Magnetic NPs with gold functionalization connect two broken electrodes in a targeted position (right). Reproduced with permission. [16] Copyright 2019, American Chemical Society. F) Avoidance of predators. A bait ball of fishes is formed during the hunting of a sea lion (left). Reproduced with permission. [22] Copyright 2019, Science and Information Organization. The biomimetic predator-prey behavior in a binary system of active micromotors (right). Reproduced with permission. [17] Copyright 2019, American Chemical Society.

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“…In terms of increasing organism complexity, a long-term goal of this line of work would to endow agents with "brains"-small neural networks that take in some sensory information from the environment and then determine how the agent will attempt to move. In such a context evolution would act on the structure of the brain, similar to the case of Braitenberg vehicles [62,63] (as opposed to determining the synaptic weights in neural network models such as in [64]). In practice, this will be a challenging direction due to the large phenotype space and the separation of timescales involved in sensory processing and evolution.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Evolution of innate behavioral strategies through competitive population dynamics

Liang

Brinkman

2022

PLoS Comput Biol

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Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a simulated population of organisms. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the derivation of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

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Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Cited by 8 publications

References 39 publications

An improved braitenberg policy for object tracking

An improved braitenberg policy for object tracking

Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales

Evolution of innate behavioral strategies through competitive population dynamics

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