scholarly journals Early stages of sensorimotor map acquisition: learning with free exploration, without active movement or global structure

2019 ◽  
Vol 122 (4) ◽  
pp. 1708-1720
Author(s):  
F. T. van Vugt ◽  
D. J. Ostry
Keyword(s):  
Error Correction ◽  
Musical Instrument ◽  
Learning Processes ◽  
Active Movement ◽  
Robot Arm ◽  
Map Learning ◽  
Robust Learning ◽  
Free Exploration ◽  
Active Motor ◽  
Sensorimotor Map

One of the puzzles of learning to talk or play a musical instrument is how we learn which movement produces a particular sound: an audiomotor map. The initial stages of map acquisition can be studied by having participants learn arm movements to auditory targets. The key question is what mechanism drives this early learning. Three learning processes from previous literature were tested: map learning may rely on active motor outflow (target), on error correction, and on the correspondence between sensory and motor distances (i.e., that similar movements map to similar sounds). Alternatively, we hypothesized that map learning can proceed without these. Participants made movements that were mapped to sounds in a number of different conditions that each precluded one of the potential learning processes. We tested whether map learning relies on assumptions about topological continuity by exposing participants to a permuted map that did not preserve distances in auditory and motor space. Further groups were tested who passively experienced the targets, kinematic trajectories produced by a robot arm, and auditory feedback as a yoked active participant (hence without active motor outflow). Another group made movements without receiving targets (thus without experiencing errors). In each case we observed substantial learning, therefore none of the three hypothesized processes is required for learning. Instead early map acquisition can occur with free exploration without target error correction, is based on sensory-to-sensory correspondences, and possible even for discontinuous maps. The findings are consistent with the idea that early sensorimotor map formation can involve instance-specific learning. NEW & NOTEWORTHY This study tested learning of novel sensorimotor maps in a variety of unusual circumstances, including learning a mapping that was permuted in such as way that it fragmented the sensorimotor workspace into discontinuous parts, thus not preserving sensory and motor topology. Participants could learn this mapping, and they could learn without motor outflow or targets. These results point to a robust learning mechanism building on individual instances, inspired from machine learning literature.

The von Neumann threshold of self-reproducing systems: theory and application

Robotica ◽  
2011 ◽  
Vol 29 (1) ◽  
pp. 123-135 ◽  
Author(s):  
Pierre T. Kabamba ◽  
Patrick D. Owens ◽  
A. Galip Ulsoy
Keyword(s):  
Error Correction ◽  
Self Assembly ◽  
Degrees Of Freedom ◽  
Minimum Length ◽  
Generation Process ◽  
Robot Arm ◽  
Von Neumann ◽  
Novel Theory ◽  
Reproduction Process ◽  
Simulation Results

SUMMARYThis paper is devoted to the study of systems of entities that are capable of generating other entities of the same kind and, possibly, self-reproducing. The main technical issue addressed is to quantify the requirements that such entities must meet to be able to produce a progeny that is not degenerative, i.e., that has the same reproductive capability as the progenitor. A novel theory that allows an explicit quantification of these requirements is presented. The notion of generation rank of an entity is introduced, and it is proved that the generation process, in most cases, is degenerative in that it strictly and irreversibly decreases the generation rank from parent to descendent. It is also proved that there exists a threshold of rank such that this degeneracy can be avoided if and only if the entity has a generation rank that meets that threshold – this is the von Neumann rank threshold. On the basis of this threshold, an information threshold is derived, which quantifies the minimum amount of information that must be provided to specify an entity such that its descendents are not degenerative. Furthermore, a complexity threshold is obtained, which quantifies the minimum length of the description of that entity in a given language. As an application, self-assembly for a 2 Degrees of Freedom planar robot is considered, and simulation results are presented. A robot arm capable of picking up and placing the components of another arm, in the presence of errors, is considered to have successfully reproduced if these are placed within an allowable tolerance. The example shows that, due to the kinematics of the robot, errors can grow from one generation to the next, until the reproduction process fails eventually. However, error correction (via error sensing and feedback control) can then be used to prevent such degeneracy. The von Neumann generation rank and information thresholds are computed for this example, and are consistent with the simulation results in predicting degeneracy in the case without error correction, and predicting successful self-reproduction in the case with error correction.

scholarly journals Prediction of primary somatosensory neuron activity during active tactile exploration

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dario Campagner ◽  
Mathew Hywel Evans ◽  
Michael Ross Bale ◽  
Andrew Erskine ◽  
Rasmus Strange Petersen
Keyword(s):  
High Speed ◽  
Linear Models ◽  
Neuron Activity ◽  
Active Movement ◽  
Primary Neuron ◽  
Sense Organs ◽  
Generalised Linear Models ◽  
Free Air ◽  
Active Motor ◽  
Force Relationship

Primary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neurons of awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by whisker angle, but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are in apparent contrast to previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of sense organs.

Self-Reproducing Machines: Preventing Degeneracy

2006 ◽  
Author(s):  
Patrick D. Owens ◽  
A. Galip Ulsoy
Keyword(s):  
Error Correction ◽  
Kinematic Model ◽  
The Self ◽  
Minimal Amount ◽  
Robot Arm ◽  
Von Neumann ◽  
Component Placement ◽  
Reproduction System ◽  
Reproduction Process ◽  
Constructive Work

Machines produced by humans exhibit insufficient complexity to produce similar machines. As John von Neumann originally postulated, if biological systems are able to successfully reproduce, then there must be some characteristic that we can embed in machines to give them the ability to reproduce. Such a self-reproductive machine, also imbued with the ability to do constructive work, could prove enormously useful to the human race. This paper considers a simple self-reproducing machine, which consists of a 2-DOF, planar robot arm capable of picking up and placing the components of another arm. If the robot places the components within the allowable tolerance, then the original arm has successfully reproduced. An assembly line is constructed, so that a self-reproduction process can proceed along a track. If this process eventually fails because one robot is not capable of assembling another, then the system is said to be degenerate. Otherwise, the system is sustainable. A kinematic model that maps component placement errors from one generation of the robot arm to the next was derived. The system exhibited exponential growth in component placement errors. Thus, this self-reproduction system is degenerate. This system is then augmented to provide error-correction during the assembly process. With the application of error-correction the self-reproduction process is made sustainable. The minimal amount of error-correction required to achieve sustainable self-reproduction was investigated through sensor quantization, and it was shown that the amount of fidelity in the error-correction signal determines the success of the self-reproduction process. This self-reproduction system was also analyzed in the context of Kabamba's Generation Theory, which could predict the results obtained through simulation regarding degeneracy or sustainability.

scholarly journals The Beautiful Risk of Collaborative and Interdisciplinary Research. A Challenging Collaborative and Critical Approach toward Sustainable Learning Processes in Academic Profession

2021 ◽  
Vol 13 (9) ◽  
pp. 4723
Author(s):  
Jonas Christensen ◽  
Nils Ekelund ◽  
Margareta Melin ◽  
Pär Widén
Keyword(s):  
Professional Learning ◽  
Interdisciplinary Research ◽  
Collaborative Research ◽  
Research Process ◽  
Learning Processes ◽  
Critical Approach ◽  
Robust Learning ◽  
Challenge Based Learning ◽  
Academic System

In this article, we aim to identify and explore possibilities and challenges of academic interdisciplinary capacities and ethos. The objective is that this knowledge could be used both in future interdisciplinary research projects and in educational settings. We achieve this through self-reflective learning processes among a group of interdisciplinary scholars from four distinctly different subjects. The method used is an autoethnographic and empirical self-reflective approach to data collection, analysis and deconstruction of professional learning processes. This also serves to establish research methodological trustworthiness and authenticity. The results show that interdisciplinarity is undervalued by grant-giving institutions and the academic system, in general. It also entails time-consuming and risky research practices. However, interdisciplinary and collaborative research creates a more innovative and stimulating learning environment and enforces new ways of thinking and doing, in ascertaining each individual’s knowledge and competences. We argue that a long-term interdisciplinary and collaborative research process could enhance and raise a critical thinking and creative consciousness among scholars, contributing to a more holistic, sustainable and socially robust learning in research and higher education. Finally, we conclude that this academic interdisciplinary capacity and ethos could be framed and enhanced by the notion of Challenge-Based Learning.

scholarly journals Positioning Error Correction of Autonomusly Movable Robot Arm

2020 ◽  
pp. 152-160
Author(s):  
Kristo VAHER ◽  
Tauno OTTO ◽  
Jüri RIIVES
Keyword(s):  
Error Correction ◽  
Positioning Error ◽  
Robot Arm

scholarly journals Prediction of Primary Somatosensory Neuron Activity During Active Tactile Exploration

2015 ◽  
Author(s):  
Dario Campagner ◽  
Mathew Evans ◽  
Michael R. Bale ◽  
Andrew Erskine ◽  
Rasmus S. Petersen
Keyword(s):  
High Speed ◽  
Linear Models ◽  
Neuron Activity ◽  
Active Movement ◽  
Primary Neuron ◽  
Sense Organs ◽  
Generalised Linear Models ◽  
Free Air ◽  
Active Motor ◽  
Force Relationship

ABSTRACTPrimary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neuronsof awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by kinematics but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are discrepant with previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of the sense organs.

scholarly journals The Structure and Acquisition of Sensorimotor Maps

2018 ◽  
Vol 30 (3) ◽  
pp. 290-306 ◽  
Author(s):  
Floris T. van Vugt ◽  
David J. Ostry
Keyword(s):  
Computational Model ◽  
Error Correction ◽  
Learning Process ◽  
Arm Movements ◽  
Sensorimotor Control ◽  
Musical Instrument ◽  
Performance Improvements ◽  
Computer Mouse ◽  
Changes Over Time ◽  
Over Time

One of the puzzles of learning to talk or play a musical instrument is how we learn which movement produces a particular sound: an audiomotor map. Existing research has used mappings that are already well learned such as controlling a cursor using a computer mouse. By contrast, the acquisition of novel sensorimotor maps was studied by having participants learn arm movements to auditory targets. These sounds did not come from different directions but, like speech, were only distinguished by their frequencies. It is shown that learning involves forming not one but two maps: a point map connecting sensory targets with motor commands and an error map linking sensory errors to motor corrections. Learning a point map is possible even when targets never repeat. Thus, although participants make errors, there is no opportunity to correct them because the target is different on every trial, and therefore learning cannot be driven by error correction. Furthermore, when the opportunity for error correction is provided, it is seen that acquiring error correction is itself a learning process that changes over time and results in an error map. In principle, the error map could be derived from the point map, but instead, these two maps are independently acquired and jointly enable sensorimotor control and learning. A computational model shows that this dual encoding is optimal and simulations based on this architecture predict that learning the two maps results in performance improvements comparable with those observed empirically.

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