---
_id: '5584'
abstract:
- lang: eng
  text: "This package contains data for the publication \"Nonlinear decoding of a
    complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
    \r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
    cells that pass stability and quality criteria, recorded on the multi-electrode
    array, in response to the presentation of the complex movie with many randomly
    moving dark discs. The responses are represented as 648000 x 91 binary matrix,
    where the first index indicates the timebin of duration 12.5 ms, and the second
    index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
    in the particular time bin.\r\n(ii) README file and a graphical illustration of
    the structure of the experiment, specifying how the 648000 timebins are split
    into epochs where 1, 2, 4, or 10 discs  were displayed, and which stimulus segments
    are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
    of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
    frame, with time that is locked to the recorded raster. The luminance traces are
    produced as described in the manuscript by filtering the raw disc movie with a
    small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
  full_name: Deny, Stephane
  last_name: Deny
- first_name: Olivier
  full_name: Marre, Olivier
  last_name: Marre
- first_name: Vicente
  full_name: Botella-Soler, Vicente
  last_name: Botella-Soler
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Gasper
  full_name: Tkacik, Gasper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkacik
  orcid: 0000-0002-6699-1455
citation:
  ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
    of a complex movie from the mammalian retina. 2018. doi:<a href="https://doi.org/10.15479/AT:ISTA:98">10.15479/AT:ISTA:98</a>
  apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &#38; Tkačik, G. (2018).
    Nonlinear decoding of a complex movie from the mammalian retina. Institute of
    Science and Technology Austria. <a href="https://doi.org/10.15479/AT:ISTA:98">https://doi.org/10.15479/AT:ISTA:98</a>
  chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
    and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
    Institute of Science and Technology Austria, 2018. <a href="https://doi.org/10.15479/AT:ISTA:98">https://doi.org/10.15479/AT:ISTA:98</a>.
  ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
    decoding of a complex movie from the mammalian retina.” Institute of Science and
    Technology Austria, 2018.
  ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
    of a complex movie from the mammalian retina, Institute of Science and Technology
    Austria, <a href="https://doi.org/10.15479/AT:ISTA:98">10.15479/AT:ISTA:98</a>.
  mla: Deny, Stephane, et al. <i>Nonlinear Decoding of a Complex Movie from the Mammalian
    Retina</i>. Institute of Science and Technology Austria, 2018, doi:<a href="https://doi.org/10.15479/AT:ISTA:98">10.15479/AT:ISTA:98</a>.
  short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2024-02-21T13:45:26Z
day: '29'
ddc:
- '570'
department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
file:
- access_level: open_access
  checksum: 6808748837b9afbbbabc2a356ca2b88a
  content_type: application/octet-stream
  creator: system
  date_created: 2018-12-12T13:02:24Z
  date_updated: 2020-07-14T12:47:07Z
  file_id: '5590'
  file_name: IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat
  file_size: 1142543971
  relation: main_file
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  checksum: d6d6cd07743038fe3a12352983fcf9dd
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T13:02:25Z
  date_updated: 2020-07-14T12:47:07Z
  file_id: '5591'
  file_name: IST-2018-98-v1+2_ExperimentStructure.pdf
  file_size: 702336
  relation: main_file
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  checksum: 0c9cfb4dab35bb3dc25a04395600b1c8
  content_type: application/octet-stream
  creator: system
  date_created: 2018-12-12T13:02:26Z
  date_updated: 2020-07-14T12:47:07Z
  file_id: '5592'
  file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat
  file_size: 432
  relation: main_file
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  checksum: 2a83b011012e21e934b4596285b1a183
  content_type: text/plain
  creator: system
  date_created: 2018-12-12T13:02:26Z
  date_updated: 2020-07-14T12:47:07Z
  file_id: '5593'
  file_name: IST-2018-98-v1+4_README.txt
  file_size: 986
  relation: main_file
file_date_updated: 2020-07-14T12:47:07Z
has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: P 25651-N26
  name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
related_material:
  record:
  - id: '292'
    relation: used_in_publication
    status: public
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
  image: /images/cc_0.png
  legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
  name: Creative Commons Public Domain Dedication (CC0 1.0)
  short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '6012'
abstract:
- lang: eng
  text: We present an approach to identify concise equations from data using a shallow
    neural network approach. In contrast to ordinary black-box regression, this approach
    allows understanding functional relations and generalizing them from observed
    data to unseen parts of the parameter space. We show how to extend the class of
    learnable equations for a recently proposed equation learning network to include
    divisions, and we improve the learning and model selection strategy to be useful
    for challenging real-world data. For systems governed by analytical expressions,
    our method can in many cases identify the true underlying equation and extrapolate
    to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum
    system, where only 2 random rollouts are required to learn the forward dynamics
    and successfully achieve the swing-up task.
article_processing_charge: No
arxiv: 1
author:
- first_name: Subham
  full_name: Sahoo, Subham
  last_name: Sahoo
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
citation:
  ama: 'Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control.
    In: <i>Proceedings of the 35th International Conference on Machine Learning</i>.
    Vol 80. ML Research Press; 2018:4442-4450.'
  apa: 'Sahoo, S., Lampert, C., &#38; Martius, G. S. (2018). Learning equations for
    extrapolation and control. In <i>Proceedings of the 35th International Conference
    on Machine Learning</i> (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research
    Press.'
  chicago: Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations
    for Extrapolation and Control.” In <i>Proceedings of the 35th International Conference
    on Machine Learning</i>, 80:4442–50. ML Research Press, 2018.
  ieee: S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation
    and control,” in <i>Proceedings of the 35th International Conference on Machine
    Learning</i>, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
  ista: 'Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation
    and control. Proceedings of the 35th International Conference on Machine Learning.
    ICML: International Conference on Machine Learning vol. 80, 4442–4450.'
  mla: Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” <i>Proceedings
    of the 35th International Conference on Machine Learning</i>, vol. 80, ML Research
    Press, 2018, pp. 4442–50.
  short: S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International
    Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
conference:
  end_date: 2018-07-15
  location: Stockholm, Sweden
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2018-07-10
date_created: 2019-02-14T15:21:07Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-10-17T09:50:53Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
  arxiv:
  - '1806.07259'
  isi:
  - '000683379204058'
intvolume: '        80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1806.07259
month: '02'
oa: 1
oa_version: Preprint
page: 4442-4450
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 35th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
  link:
  - description: News on IST Homepage
    relation: press_release
    url: https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/
scopus_import: '1'
status: public
title: Learning equations for extrapolation and control
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '6841'
abstract:
- lang: eng
  text: In classical machine learning, regression is treated as a black box process
    of identifying a suitable function from a hypothesis set without attempting to
    gain insight into the mechanism connecting inputs and outputs. In the natural
    sciences, however, finding an interpretable function for a phenomenon is the prime
    goal as it allows to understand and generalize results. This paper proposes a
    novel type of function learning network, called equation learner (EQL), that can
    learn analytical expressions and is able to extrapolate to unseen domains. It
    is implemented as an end-to-end differentiable feed-forward network and allows
    for efficient gradient based training. Due to sparsity regularization concise
    interpretable expressions can be obtained. Often the true underlying source expression
    is identified.
arxiv: 1
author:
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Martius GS, Lampert C. Extrapolation and learning equations. In: <i>5th International
    Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>.
    International Conference on Learning Representations; 2017.'
  apa: 'Martius, G. S., &#38; Lampert, C. (2017). Extrapolation and learning equations.
    In <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop
    Track Proceedings</i>. Toulon, France: International Conference on Learning Representations.'
  chicago: Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.”
    In <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop
    Track Proceedings</i>. International Conference on Learning Representations, 2017.
  ieee: G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in <i>5th
    International Conference on Learning Representations, ICLR 2017 - Workshop Track
    Proceedings</i>, Toulon, France, 2017.
  ista: 'Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International
    Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
    ICLR: International Conference on Learning Representations.'
  mla: Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.”
    <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop
    Track Proceedings</i>, International Conference on Learning Representations, 2017.
  short: G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations,
    ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations,
    2017.
conference:
  end_date: 2017-04-26
  location: Toulon, France
  name: 'ICLR: International Conference on Learning Representations'
  start_date: 2017-04-24
date_created: 2019-09-01T22:01:00Z
date_published: 2017-02-21T00:00:00Z
date_updated: 2021-01-12T08:09:17Z
day: '21'
department:
- _id: ChLa
ec_funded: 1
external_id:
  arxiv:
  - '1610.02995'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1610.02995
month: '02'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication: 5th International Conference on Learning Representations, ICLR 2017 -
  Workshop Track Proceedings
publication_status: published
publisher: International Conference on Learning Representations
quality_controlled: '1'
scopus_import: 1
status: public
title: Extrapolation and learning equations
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '652'
abstract:
- lang: eng
  text: 'We present an approach that enables robots to self-organize their sensorimotor
    behavior from scratch without providing specific information about neither the
    robot nor its environment. This is achieved by a simple neural control law that
    increases the consistency between external sensor dynamics and internal neural
    dynamics of the utterly simple controller. In this way, the embodiment and the
    agent-environment coupling are the only source of individual development. We show
    how an anthropomorphic tendon driven arm-shoulder system develops different behaviors
    depending on that coupling. For instance: Given a bottle half-filled with water,
    the arm starts to shake it, driven by the physical response of the water. When
    attaching a brush, the arm can be manipulated into wiping a table, and when connected
    to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said
    to discover the affordances of the world. When allowing two (simulated) humanoid
    robots to interact physically, they engage into a joint behavior development leading
    to, for instance, spontaneous cooperation. More social effects are observed if
    the robots can visually perceive each other. Although, as an observer, it is tempting
    to attribute an apparent intentionality, there is nothing of the kind put in.
    As a conclusion, we argue that emergent behavior may be much less rooted in explicit
    intentions, internal motivations, or specific reward systems than is commonly
    believed.'
article_number: '7846789'
author:
- first_name: Ralf
  full_name: Der, Ralf
  last_name: Der
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
citation:
  ama: 'Der R, Martius GS. Dynamical self consistency leads to behavioral development
    and emergent social interactions in robots. In: IEEE; 2017. doi:<a href="https://doi.org/10.1109/DEVLRN.2016.7846789">10.1109/DEVLRN.2016.7846789</a>'
  apa: 'Der, R., &#38; Martius, G. S. (2017). Dynamical self consistency leads to
    behavioral development and emergent social interactions in robots. Presented at
    the ICDL EpiRob: International Conference on Development and Learning and Epigenetic
    Robotics , Cergy-Pontoise, France: IEEE. <a href="https://doi.org/10.1109/DEVLRN.2016.7846789">https://doi.org/10.1109/DEVLRN.2016.7846789</a>'
  chicago: Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral
    Development and Emergent Social Interactions in Robots.” IEEE, 2017. <a href="https://doi.org/10.1109/DEVLRN.2016.7846789">https://doi.org/10.1109/DEVLRN.2016.7846789</a>.
  ieee: 'R. Der and G. S. Martius, “Dynamical self consistency leads to behavioral
    development and emergent social interactions in robots,” presented at the ICDL
    EpiRob: International Conference on Development and Learning and Epigenetic Robotics
    , Cergy-Pontoise, France, 2017.'
  ista: 'Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development
    and emergent social interactions in robots. ICDL EpiRob: International Conference
    on Development and Learning and Epigenetic Robotics , 7846789.'
  mla: Der, Ralf, and Georg S. Martius. <i>Dynamical Self Consistency Leads to Behavioral
    Development and Emergent Social Interactions in Robots</i>. 7846789, IEEE, 2017,
    doi:<a href="https://doi.org/10.1109/DEVLRN.2016.7846789">10.1109/DEVLRN.2016.7846789</a>.
  short: R. Der, G.S. Martius, in:, IEEE, 2017.
conference:
  end_date: 2016-09-22
  location: Cergy-Pontoise, France
  name: 'ICDL EpiRob: International Conference on Development and Learning and Epigenetic
    Robotics '
  start_date: 2016-09-19
date_created: 2018-12-11T11:47:43Z
date_published: 2017-02-07T00:00:00Z
date_updated: 2021-01-12T08:07:51Z
day: '07'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/DEVLRN.2016.7846789
language:
- iso: eng
month: '02'
oa_version: None
publication_identifier:
  isbn:
  - 978-150905069-7
publication_status: published
publisher: IEEE
publist_id: '7100'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamical self consistency leads to behavioral development and emergent social
  interactions in robots
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '658'
abstract:
- lang: eng
  text: 'With the accelerated development of robot technologies, control becomes one
    of the central themes of research. In traditional approaches, the controller,
    by its internal functionality, finds appropriate actions on the basis of specific
    objectives for the task at hand. While very successful in many applications, self-organized
    control schemes seem to be favored in large complex systems with unknown dynamics
    or which are difficult to model. Reasons are the expected scalability, robustness,
    and resilience of self-organizing systems. The paper presents a self-learning
    neurocontroller based on extrinsic differential plasticity introduced recently,
    applying it to an anthropomorphic musculoskeletal robot arm with attached objects
    of unknown physical dynamics. The central finding of the paper is the following
    effect: by the mere feedback through the internal dynamics of the object, the
    robot is learning to relate each of the objects with a very specific sensorimotor
    pattern. Specifically, an attached pendulum pilots the arm into a circular motion,
    a half-filled bottle produces axis oriented shaking behavior, a wheel is getting
    rotated, and wiping patterns emerge automatically in a table-plus-brush setting.
    By these object-specific dynamical patterns, the robot may be said to recognize
    the object''s identity, or in other words, it discovers dynamical affordances
    of objects. Furthermore, when including hand coordinates obtained from a camera,
    a dedicated hand-eye coordination self-organizes spontaneously. These phenomena
    are discussed from a specific dynamical system perspective. Central is the dedicated
    working regime at the border to instability with its potentially infinite reservoir
    of (limit cycle) attractors &quot;waiting&quot; to be excited. Besides converging
    toward one of these attractors, variate behavior is also arising from a self-induced
    attractor morphing driven by the learning rule. We claim that experimental investigations
    with this anthropomorphic, self-learning robot not only generate interesting and
    potentially useful behaviors, but may also help to better understand what subjective
    human muscle feelings are, how they can be rooted in sensorimotor patterns, and
    how these concepts may feed back on robotics.'
article_number: '00008'
article_processing_charge: Yes
author:
- first_name: Ralf
  full_name: Der, Ralf
  last_name: Der
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
citation:
  ama: Der R, Martius GS. Self organized behavior generation for musculoskeletal robots.
    <i>Frontiers in Neurorobotics</i>. 2017;11(MAR). doi:<a href="https://doi.org/10.3389/fnbot.2017.00008">10.3389/fnbot.2017.00008</a>
  apa: Der, R., &#38; Martius, G. S. (2017). Self organized behavior generation for
    musculoskeletal robots. <i>Frontiers in Neurorobotics</i>. Frontiers Research
    Foundation. <a href="https://doi.org/10.3389/fnbot.2017.00008">https://doi.org/10.3389/fnbot.2017.00008</a>
  chicago: Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for
    Musculoskeletal Robots.” <i>Frontiers in Neurorobotics</i>. Frontiers Research
    Foundation, 2017. <a href="https://doi.org/10.3389/fnbot.2017.00008">https://doi.org/10.3389/fnbot.2017.00008</a>.
  ieee: R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal
    robots,” <i>Frontiers in Neurorobotics</i>, vol. 11, no. MAR. Frontiers Research
    Foundation, 2017.
  ista: Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal
    robots. Frontiers in Neurorobotics. 11(MAR), 00008.
  mla: Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal
    Robots.” <i>Frontiers in Neurorobotics</i>, vol. 11, no. MAR, 00008, Frontiers
    Research Foundation, 2017, doi:<a href="https://doi.org/10.3389/fnbot.2017.00008">10.3389/fnbot.2017.00008</a>.
  short: R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).
date_created: 2018-12-11T11:47:45Z
date_published: 2017-03-16T00:00:00Z
date_updated: 2021-01-12T08:08:04Z
day: '16'
ddc:
- '006'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3389/fnbot.2017.00008
ec_funded: 1
file:
- access_level: open_access
  checksum: b1bc43f96d1df3313c03032c2a46388d
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:18:49Z
  date_updated: 2020-07-14T12:47:33Z
  file_id: '5371'
  file_name: IST-2017-903-v1+1_fnbot-11-00008.pdf
  file_size: 8439566
  relation: main_file
file_date_updated: 2020-07-14T12:47:33Z
has_accepted_license: '1'
intvolume: '        11'
issue: MAR
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Frontiers in Neurorobotics
publication_identifier:
  issn:
  - '16625218'
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '7078'
pubrep_id: '903'
quality_controlled: '1'
scopus_import: 1
status: public
title: Self organized behavior generation for musculoskeletal robots
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2017'
...
---
_id: '8094'
abstract:
- lang: eng
  text: 'With the accelerated development of robot technologies, optimal control becomes
    one of the central themes of research. In traditional approaches, the controller,
    by its internal functionality, finds appropriate actions on the basis of the history
    of sensor values, guided by the goals, intentions, objectives, learning schemes,
    and so forth. The idea is that the controller controls the world---the body plus
    its environment---as reliably as possible. This paper focuses on new lines of
    self-organization for developmental robotics. We apply the recently developed
    differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
    system from the Myorobotics toolkit. In the experiments, we observe a vast variety
    of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
    sequences of different poses. By applying physical forces, the system can be entrained
    into definite motion patterns like wiping a table. Most interestingly, after attaching
    an object, the controller gets in a functional resonance with the object''s internal
    dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
    driving an attached pendulum into a circular mode. When attached to the crank
    of a wheel the neural system independently discovers how to rotate it. In this
    way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Rafael
  full_name: Hostettler, Rafael
  last_name: Hostettler
- first_name: Alois
  full_name: Knoll, Alois
  last_name: Knoll
- first_name: Ralf
  full_name: Der, Ralf
  last_name: Der
citation:
  ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
    driven arm by differential extrinsic plasticity. In: <i>Proceedings of the Artificial
    Life Conference 2016</i>. Vol 28. MIT Press; 2016:142-143. doi:<a href="https://doi.org/10.7551/978-0-262-33936-0-ch029">10.7551/978-0-262-33936-0-ch029</a>'
  apa: 'Martius, G. S., Hostettler, R., Knoll, A., &#38; Der, R. (2016). Self-organized
    control of an tendon driven arm by differential extrinsic plasticity. In <i>Proceedings
    of the Artificial Life Conference 2016</i> (Vol. 28, pp. 142–143). Cancun, Mexico:
    MIT Press. <a href="https://doi.org/10.7551/978-0-262-33936-0-ch029">https://doi.org/10.7551/978-0-262-33936-0-ch029</a>'
  chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
    Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In <i>Proceedings
    of the Artificial Life Conference 2016</i>, 28:142–43. MIT Press, 2016. <a href="https://doi.org/10.7551/978-0-262-33936-0-ch029">https://doi.org/10.7551/978-0-262-33936-0-ch029</a>.
  ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
    of an tendon driven arm by differential extrinsic plasticity,” in <i>Proceedings
    of the Artificial Life Conference 2016</i>, Cancun, Mexico, 2016, vol. 28, pp.
    142–143.
  ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
    an tendon driven arm by differential extrinsic plasticity. Proceedings of the
    Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on
    the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
  mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
    Differential Extrinsic Plasticity.” <i>Proceedings of the Artificial Life Conference
    2016</i>, vol. 28, MIT Press, 2016, pp. 142–43, doi:<a href="https://doi.org/10.7551/978-0-262-33936-0-ch029">10.7551/978-0-262-33936-0-ch029</a>.
  short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial
    Life Conference 2016, MIT Press, 2016, pp. 142–143.
conference:
  end_date: 2016-07-08
  location: Cancun, Mexico
  name: 'ALIFE 2016: 15th International Conference on the Synthesis and Simulation
    of Living Systems'
  start_date: 2016-07-04
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T08:16:53Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
  checksum: cff63e7a4b8ac466ba51a9c84153a940
  content_type: application/pdf
  creator: cziletti
  date_created: 2020-07-06T12:59:09Z
  date_updated: 2020-07-14T12:48:09Z
  file_id: '8096'
  file_name: 2016_ProcALIFE_Martius.pdf
  file_size: 678670
  relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: '        28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Proceedings of the Artificial Life Conference 2016
publication_identifier:
  isbn:
  - '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: 1
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic plasticity
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 28
year: '2016'
...
---
_id: '1214'
abstract:
- lang: eng
  text: 'With the accelerated development of robot technologies, optimal control becomes
    one of the central themes of research. In traditional approaches, the controller,
    by its internal functionality, finds appropriate actions on the basis of the history
    of sensor values, guided by the goals, intentions, objectives, learning schemes,
    and so forth. While very successful with classical robots, these methods run into
    severe difficulties when applied to soft robots, a new field of robotics with
    large interest for human-robot interaction. We claim that a novel controller paradigm
    opens new perspective for this field. This paper applies a recently developed
    neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon
    driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we
    observe a vast variety of self-organized behavior patterns: when left alone, the
    arm realizes pseudo-random sequences of different poses. By applying physical
    forces, the system can be entrained into definite motion patterns like wiping
    a table. Most interestingly, after attaching an object, the controller gets in
    a functional resonance with the object''s internal dynamics, starting to shake
    spontaneously bottles half-filled with water or sensitively driving an attached
    pendulum into a circular mode. When attached to the crank of a wheel the neural
    system independently develops to rotate it. In this way, the robot discovers affordances
    of objects its body is interacting with.'
acknowledgement: RD thanks for the hospitality at the Max-Planck-Institute and for
  helpful discussions with Nihat Ay and Keyan Zahedi.
article_number: '7759138'
author:
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Raphael
  full_name: Hostettler, Raphael
  last_name: Hostettler
- first_name: Alois
  full_name: Knoll, Alois
  last_name: Knoll
- first_name: Ralf
  full_name: Der, Ralf
  last_name: Der
citation:
  ama: 'Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots:
    Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November.
    IEEE; 2016. doi:<a href="https://doi.org/10.1109/IROS.2016.7759138">10.1109/IROS.2016.7759138</a>'
  apa: 'Martius, G. S., Hostettler, R., Knoll, A., &#38; Der, R. (2016). Compliant
    control for soft robots: Emergent behavior of a tendon driven anthropomorphic
    arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on
    Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. <a href="https://doi.org/10.1109/IROS.2016.7759138">https://doi.org/10.1109/IROS.2016.7759138</a>'
  chicago: 'Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant
    Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic
    Arm,” Vol. 2016–November. IEEE, 2016. <a href="https://doi.org/10.1109/IROS.2016.7759138">https://doi.org/10.1109/IROS.2016.7759138</a>.'
  ieee: 'G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for
    soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented
    at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS
    , Daejeon, Korea, 2016, vol. 2016–November.'
  ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft
    robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International
    Conference on Intelligent Robots and Systems IROS  vol. 2016–November, 7759138.'
  mla: 'Martius, Georg S., et al. <i>Compliant Control for Soft Robots: Emergent Behavior
    of a Tendon Driven Anthropomorphic Arm</i>. Vol. 2016–November, 7759138, IEEE,
    2016, doi:<a href="https://doi.org/10.1109/IROS.2016.7759138">10.1109/IROS.2016.7759138</a>.'
  short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.
conference:
  end_date: 2016-09-14
  location: Daejeon, Korea
  name: 'IEEE RSJ International Conference on Intelligent Robots and Systems IROS '
  start_date: 2016-09-09
date_created: 2018-12-11T11:50:45Z
date_published: 2016-11-28T00:00:00Z
date_updated: 2021-01-12T06:49:08Z
day: '28'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/IROS.2016.7759138
language:
- iso: eng
month: '11'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '6121'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic
  arm'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-November
year: '2016'
...
---
_id: '1655'
abstract:
- lang: eng
  text: Quantifying behaviors of robots which were generated autonomously from task-independent
    objective functions is an important prerequisite for objective comparisons of
    algorithms and movements of animals. The temporal sequence of such a behavior
    can be considered as a time series and hence complexity measures developed for
    time series are natural candidates for its quantification. The predictive information
    and the excess entropy are such complexity measures. They measure the amount of
    information the past contains about the future and thus quantify the nonrandom
    structure in the temporal sequence. However, when using these measures for systems
    with continuous states one has to deal with the fact that their values will depend
    on the resolution with which the systems states are observed. For deterministic
    systems both measures will diverge with increasing resolution. We therefore propose
    a new decomposition of the excess entropy in resolution dependent and resolution
    independent parts and discuss how they depend on the dimensionality of the dynamics,
    correlations and the noise level. For the practical estimation we propose to use
    estimates based on the correlation integral instead of the direct estimation of
    the mutual information based on next neighbor statistics because the latter allows
    less control of the scale dependencies. Using our algorithm we are able to show
    how autonomous learning generates behavior of increasing complexity with increasing
    learning duration.
acknowledgement: This work was supported by the DFG priority program 1527 (Autonomous
  Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013)
  under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie
  Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013)
  under REA grant agreement no. 291734.
article_processing_charge: No
author:
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Eckehard
  full_name: Olbrich, Eckehard
  last_name: Olbrich
citation:
  ama: Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots.
    <i>Entropy</i>. 2015;17(10):7266-7297. doi:<a href="https://doi.org/10.3390/e17107266">10.3390/e17107266</a>
  apa: Martius, G. S., &#38; Olbrich, E. (2015). Quantifying emergent behavior of
    autonomous robots. <i>Entropy</i>. MDPI. <a href="https://doi.org/10.3390/e17107266">https://doi.org/10.3390/e17107266</a>
  chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior
    of Autonomous Robots.” <i>Entropy</i>. MDPI, 2015. <a href="https://doi.org/10.3390/e17107266">https://doi.org/10.3390/e17107266</a>.
  ieee: G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous
    robots,” <i>Entropy</i>, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
  ista: Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots.
    Entropy. 17(10), 7266–7297.
  mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of
    Autonomous Robots.” <i>Entropy</i>, vol. 17, no. 10, MDPI, 2015, pp. 7266–97,
    doi:<a href="https://doi.org/10.3390/e17107266">10.3390/e17107266</a>.
  short: G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.
date_created: 2018-12-11T11:53:17Z
date_published: 2015-10-23T00:00:00Z
date_updated: 2023-10-17T11:42:00Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3390/e17107266
ec_funded: 1
file:
- access_level: open_access
  checksum: 945d99631a96e0315acb26dc8541dcf9
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:12:25Z
  date_updated: 2020-07-14T12:45:08Z
  file_id: '4943'
  file_name: IST-2016-464-v1+1_entropy-17-07266.pdf
  file_size: 6455007
  relation: main_file
file_date_updated: 2020-07-14T12:45:08Z
has_accepted_license: '1'
intvolume: '        17'
issue: '10'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 7266 - 7297
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Entropy
publication_status: published
publisher: MDPI
publist_id: '5495'
pubrep_id: '464'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying emergent behavior of autonomous robots
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
  text: Grounding autonomous behavior in the nervous system is a fundamental challenge
    for neuroscience. In particular, self-organized behavioral development provides
    more questions than answers. Are there special functional units for curiosity,
    motivation, and creativity? This paper argues that these features can be grounded
    in synaptic plasticity itself, without requiring any higher-level constructs.
    We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
    self-learning systems and apply it to a number of complex robotic systems as a
    test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
    rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
    These surprising results require no systemspecific modifications of the DEP rule.
    They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
    is due to the tight brain body environment coupling. The new synaptic rule is
    biologically plausible and would be an interesting target for neurobiological
    investigation. We also argue that this neuronal mechanism may have been a catalyst
    in natural evolution.
author:
- first_name: Ralf
  full_name: Der, Ralf
  last_name: Der
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
citation:
  ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
    intelligence. <i>PNAS</i>. 2015;112(45):E6224-E6232. doi:<a href="https://doi.org/10.1073/pnas.1508400112">10.1073/pnas.1508400112</a>
  apa: Der, R., &#38; Martius, G. S. (2015). Novel plasticity rule can explain the
    development of sensorimotor intelligence. <i>PNAS</i>. National Academy of Sciences.
    <a href="https://doi.org/10.1073/pnas.1508400112">https://doi.org/10.1073/pnas.1508400112</a>
  chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
    Development of Sensorimotor Intelligence.” <i>PNAS</i>. National Academy of Sciences,
    2015. <a href="https://doi.org/10.1073/pnas.1508400112">https://doi.org/10.1073/pnas.1508400112</a>.
  ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
    of sensorimotor intelligence,” <i>PNAS</i>, vol. 112, no. 45. National Academy
    of Sciences, pp. E6224–E6232, 2015.
  ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
    of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
  mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
    of Sensorimotor Intelligence.” <i>PNAS</i>, vol. 112, no. 45, National Academy
    of Sciences, 2015, pp. E6224–32, doi:<a href="https://doi.org/10.1073/pnas.1508400112">10.1073/pnas.1508400112</a>.
  short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
  pmid:
  - '26504200'
intvolume: '       112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '12881'
acknowledgement: This work was supported by the DFG (SPP 1527) and the EU (FP7, REA
  grant no 291734).
article_processing_charge: No
author:
- first_name: Georg S
  full_name: Martius, Georg S
  id: 3A276B68-F248-11E8-B48F-1D18A9856A87
  last_name: Martius
- first_name: Eckehard
  full_name: Olbrich, Eckehard
  last_name: Olbrich
citation:
  ama: 'Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous
    robots. In: <i>Proceedings of the 13th European Conference on Artificial Life</i>.
    MIT Press; 2015:78. doi:<a href="https://doi.org/10.7551/978-0-262-33027-5-ch018">10.7551/978-0-262-33027-5-ch018</a>'
  apa: 'Martius, G. S., &#38; Olbrich, E. (2015). Quantifying self-organizing behavior
    of autonomous robots. In <i>Proceedings of the 13th European Conference on Artificial
    Life</i> (p. 78). York, United Kingdom: MIT Press. <a href="https://doi.org/10.7551/978-0-262-33027-5-ch018">https://doi.org/10.7551/978-0-262-33027-5-ch018</a>'
  chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
    of Autonomous Robots.” In <i>Proceedings of the 13th European Conference on Artificial
    Life</i>, 78. MIT Press, 2015. <a href="https://doi.org/10.7551/978-0-262-33027-5-ch018">https://doi.org/10.7551/978-0-262-33027-5-ch018</a>.
  ieee: G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous
    robots,” in <i>Proceedings of the 13th European Conference on Artificial Life</i>,
    York, United Kingdom, 2015, p. 78.
  ista: 'Martius GS, Olbrich E. 2015. Quantifying self-organizing behavior of autonomous
    robots. Proceedings of the 13th European Conference on Artificial Life. ECAL:
    European Conference on Artificial Life, 78.'
  mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
    of Autonomous Robots.” <i>Proceedings of the 13th European Conference on Artificial
    Life</i>, MIT Press, 2015, p. 78, doi:<a href="https://doi.org/10.7551/978-0-262-33027-5-ch018">10.7551/978-0-262-33027-5-ch018</a>.
  short: G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference
    on Artificial Life, MIT Press, 2015, p. 78.
conference:
  end_date: 2015-07-24
  location: York, United Kingdom
  name: 'ECAL: European Conference on Artificial Life'
  start_date: 2015-07-20
date_created: 2023-04-30T22:01:07Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2023-05-02T07:06:21Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.7551/978-0-262-33027-5-ch018
ec_funded: 1
file:
- access_level: open_access
  checksum: 880eabe59c9df12f06a882aa1bc4e600
  content_type: application/pdf
  creator: dernst
  date_created: 2023-05-02T07:02:59Z
  date_updated: 2023-05-02T07:02:59Z
  file_id: '12882'
  file_name: 2015_ECAL_Martius.pdf
  file_size: 1674241
  relation: main_file
  success: 1
file_date_updated: 2023-05-02T07:02:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '78'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 13th European Conference on Artificial Life
publication_identifier:
  isbn:
  - '9780262330275'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying self-organizing behavior of autonomous robots
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...