Embodiment Enables Non-Predictive Ways of Coping with Self-Caused Sensory Stimuli

• URL: http://arxiv.org/abs/2205.06446v1
• Date: Fri, 13 May 2022 04:35:05 GMT
• Title: Embodiment Enables Non-Predictive Ways of Coping with Self-Caused Sensory Stimuli
• Authors: James Garner and Matthew Egbert
• Abstract summary: Living systems process sensory data to facilitate adaptive behaviour. We implement a computational model of a simple embodied system with self-caused sensorimotor dynamics. We find that solutions that regulate behaviour to control self-caused sensory inputs tend to emerge, rather than solutions which predict and filter out self-caused inputs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Living systems process sensory data to facilitate adaptive behaviour. A given sensor can be stimulated as the result of internally driven activity, or by purely external (environmental) sources. It is clear that these inputs are processed differently - have you ever tried tickling yourself? The canonical explanation of this difference is that when the brain sends a signal that would result in motor activity, it uses a copy of that signal to predict the sensory consequences of the resulting motor activity. The predicted sensory input is then subtracted from the actual sensory input, resulting in attenuation of the stimuli. To critically evaluate this idea, and investigate when non-predictive solutions may be viable, we implement a computational model of a simple embodied system with self-caused sensorimotor dynamics, and analyse how controllers successfully accomplish tasks in this model. We find that in these simple systems, solutions that regulate behaviour to control self-caused sensory inputs tend to emerge, rather than solutions which predict and filter out self-caused inputs. In some cases, solutions depend on the presence of these self-caused inputs.

Related papers

• A model-free approach to fingertip slip and disturbance detection for grasp stability inference [0.0]
  We propose a method for assessing grasp stability using tactile sensing. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method.
  arXiv  Detail & Related papers  (2023-11-22T09:04:26Z)
• What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving [55.41644538483948]
  We analyze the effects of mental workload and perceptual load on psychophysiological dimensions. We classify the mental and perceptual load levels through the fusion of these measurements. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.
  arXiv  Detail & Related papers  (2022-08-10T21:19:49Z)
• Adaptation through prediction: multisensory active inference torque control [0.0]
  We present a novel multisensory active inference torque controller for industrial arms. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches.
  arXiv  Detail & Related papers  (2021-12-13T16:03:18Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Neural optimal feedback control with local learning rules [67.5926699124528]
  A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. We introduce a novel online algorithm which combines adaptive Kalman filtering with a model free control approach.
  arXiv  Detail & Related papers  (2021-11-12T20:02:00Z)
• Sensory attenuation develops as a result of sensorimotor experience [7.109236732052832]
  We created a neural network model consisting of sensory (proprioceptive and exteroceptive), association, and executive areas. A simulated robot controlled by the network learned to acquire motor patterns with self-produced or externally produced exteroceptive feedback. We found that the robot first increased responses in sensory and association areas for both self-produced and externally produced conditions in the early stage of learning.
  arXiv  Detail & Related papers  (2021-11-04T07:12:48Z)
• Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference [3.6536977425574664]
  This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes, a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. We propose a fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery.
  arXiv  Detail & Related papers  (2021-09-13T11:14:19Z)
• Adaptive conversion of real-valued input into spike trains [91.3755431537592]
  This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks. The proposed method mimics the adaptive behaviour of retinal ganglion cells and allows input neurons to adapt their response to changes in the statistics of the input.
  arXiv  Detail & Related papers  (2021-04-12T12:33:52Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)
• Evaluating the Apperception Engine [31.071555696874054]
  Apperception Engine is an unsupervised learning system. It constructs a symbolic causal theory that both explains the sensory sequence and satisfies a set of unity conditions. It can be applied to predict future sensor readings, retrodict earlier readings, or impute missing readings.
  arXiv  Detail & Related papers  (2020-07-09T11:54:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.