Livrables Websites, patent fillings, videos etc. (1) Website This deliverable will a short report on the Website of the project. Documents, reports (17) Dissemination strategy and plan. This deliverable will report on how the Plan4Act project will set out to disseminate its results. Report on neural network model encoding sequences. This deliverable will report on the design of a neural network encoder which is able to model neural sequences belonging to the monkey recordings from WP1. This is the basic starting point of the model for emulating data from Tests 1 and 2 (see Part-B). Short report and specification data sheet about the implementation of the interfaces for the Smart House. This deliverable will provide a report in the form of a data sheet specifying the actual implementations of the required interfaces that allow connecting Smart House devices to the experimental data from WP1 via the controllers developed in WP2 and WP3. First report on the adaptable network controller for complex action sequence generation and smart house control. This deliverable will report how the adaptive control units from D3.1 can be combined into an adaptable network controller that allows encoding complex action sequence information and can be used for the controlling of a Smart House. Report on neural encoding of complex action sequences in SmartCage for Test 2 and update on Test 1. This deliverable will report how complex action sequences are encoded by the neurons in response to monkey predictive behavior in the SmartCage. This concerns Test 2 as described in Paert-B. In addition the report will provide an update on Test 1. Final report on the hardware controller and integration into the demonstrator for proactive neural-based smart house control. This deliverable is the continuation from D3.2 and represents the final report on how to create an adaptable network controller from the adaptive control units from D3.1. It shall report how to finally encode complex action sequence information with this network and how to use it for smart house control. First Report on status of experimental setup (SmartCage), training, behavioral testing, and neural recording. This deliverable will report how the Smart Cage will be set up, which functions it will support and how it shall operate in conjunction with neural recordings. It will address the training and behavioral testing aspects as well as the actual recording setup for monkey neural recordings. Report on the status of Smart House devices and interfaces for connecting to the controllers of WP2 and WP3. This deliverable will report on the different devices and interfaces that exist in the here used Smart House in order to allow us to connect them with the controllers from WP2 and WP3. Pro Memori: reporting as per Grant Agreement Covers all reporting obligations (periodic and final) as required by the Grant Agreement Report on behavioral testing and status of recording with SmartCage action sequence planning for Test 1. "This deliverable will report about the specific experiments for the action sequence planning in ""Test 1"" as described in Part-B. This concerns behavioral testing as well as the corresponding neural recordings in the SmartCage." Data sheet of definitions of experimental conditions for the Smart House as depending on the setup and data of WP1. This deliverable will provide a report in the form of a data sheet specifying the different required definitions of the experimental conditions which would allow controlling a Smart House with neural data from Tests 1 and 2 (see Part-B). Hence it provides the link to the experimental data from WP1. Report on neural network model enabling the prediction of complex planned sequences of actions. This deliverable will report on the next extension of the neural network controller to model the prediction of complex planned sequences of actions. This concerns Test 2 as described in Part-B. Final report on all dissemination activities. This deliverable will be the final report on all dissemination activities of Plan4Act Report on neural network model which predicts distractor-robustly the planned sequence of actions This deliverable will report on the final version of the neural network controller to model the data in a way which is robust against distractors in the planned sequence of actions. This concerns Tests 1 and 2 as described in Part-B. Report on neural network model using the sequential structure to predict the present sequence. This deliverable will report on the neural network model extended to be able to predict the data from Test 1 (see Part-B), which will use a sequential structure with the goal to be able to predict the present behavioral sequence. Report on generic reduced control units for complex action sequence formation. This deliverable reports on the electronic implementation and it will describe how the cell assemblies in the neural network from WP2 can be generically reduced into electronic control units that allow encoding of complex action sequence information. Report of all final specifications of the Smart House setup and interfaces. This deliverable will provide a report in the form of a data sheet and will be the continuation of D4.3 where we shall specify how all required interfaces are constituted and actually set up for Smart House control. Demonstrators, pilots, prototypes (2) Final demo of system using the hardware controller and data from Tests 1 and 2, at least for one Test an online demo is planned. This deliverable will be the final demonstrator of this project and shall show how to use the hardware controller to control Smart House devices using data from both Tests (1 and 2). It is planned to show at least for the simpler Test 1 an online demo. Demonstration of Smart House control using the software controller from WP2 and the Test 1 condition from WP1. This deliverable will be a demonstrator that shows the functionality of the software based controller from WP2 using the simpler Test 1 experimental data. Open Research Data Pilot (1) Data Management Plan This deliverable will describe the Data Managment Plan as required by the Pilot on Open Research Data. This plan will discuss: 1) What types of data will the project generate/collect? 2) What standards will be used? 3) How this data will be exploited and/or shared/made accessible for verification and re-use? And how this data will be curated and preserved? Publications Peer reviewed articles (26) AHEAD: Automatic Holistic Energy-Aware Design Methodology for MLP Neural Network Hardware Generation in Proactive BMI Edge Devices Auteurs: Nan-Sheng Huang, Yi-Chung Chen, Jørgen Christian Larsen, Poramate Manoonpong Publié dans: Energies, Issue 13/9, 2020, Page(s) 2180, ISSN 1996-1073 Éditeur: Multidisciplinary Digital Publishing Institute (MDPI) DOI: 10.3390/en13092180 The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior Auteurs: Philipp Ulbrich, Alexander Gail Publié dans: Behavior Research Methods, 2021, ISSN 1554-3528 Éditeur: Springer DOI: 10.3758/s13428-021-01579-5 Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission Auteurs: Daniel Miner, Florentin Wörgötter, Christian Tetzlaff and Michael Fauth Publié dans: Biology, 2021, ISSN 2079-7737 Éditeur: MDPI DOI: 10.3390/biology10070577 The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations Auteurs: Johannes Maria Auth, Timo Nachstedt, Christian Tetzlaff Publié dans: Frontiers in Neural Circuits, Issue 14, 2020, ISSN 1662-5110 Éditeur: Frontiers Research Foundation DOI: 10.3389/fncir.2020.541728 Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks Auteurs: Jannik Luboeinski, Christian Tetzlaff Publié dans: Communications Biology, Issue 4/1, 2021, ISSN 2399-3642 Éditeur: Nature Publishing Group DOI: 10.1038/s42003-021-01778-y Evolving artificial neural networks with feedback Auteurs: Sebastian Herzog, Christian Tetzlaff, Florentin Wörgötter Publié dans: Neural Networks, Issue 123, 2020, Page(s) 153-162, ISSN 0893-6080 Éditeur: Pergamon Press Ltd. DOI: 10.1016/j.neunet.2019.12.004 A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation Auteurs: Ezequiel Simeoni, Eugenio Gaeta, Rebeca I. García-Betances, Dave Raggett, Alejandro M. Medrano-Gil, Diego F. Carvajal-Flores, Giuseppe Fico, María Fernanda Cabrera-Umpiérrez, María Teresa Arredondo Waldmeyer Publié dans: Sensors, Issue 21/11, 2021, Page(s) 3587, ISSN 1424-8220 Éditeur: Multidisciplinary Digital Publishing Institute (MDPI) DOI: 10.3390/s21113587 Embodied Synaptic Plasticity With Online Reinforcement Learning Auteurs: Jacques Kaiser, Michael Hoff, Andreas Konle, J. Camilo Vasquez Tieck, David Kappel, Daniel Reichard, Anand Subramoney, Robert Legenstein, Arne Roennau, Wolfgang Maass, Rüdiger Dillmann Publié dans: Frontiers in Neurorobotics, Issue 13, 2019, ISSN 1662-5218 Éditeur: Frontiers Research Foundation DOI: 10.3389/fnbot.2019.00081 Humans Predict Action using Grammar-like Structures Auteurs: Wörgötter, F.; Ziaeetabar, F.; Pfeiffer, S.; Kaya, O.; Kulvicius, T.; Tamosiunaite, M. Publié dans: Scientific Reports, Issue 1, 2020, ISSN 2045-2322 Éditeur: Nature Publishing Group Generic Neural Locomotion Control Framework for Legged Robots Auteurs: Mathias Thor, Tomas Kulvicius, Poramate Manoonpong Publié dans: IEEE Transactions on Neural Networks and Learning Systems, 2020, Page(s) 1-13, ISSN 2162-237X Éditeur: IEEE Computational Intelligence Society DOI: 10.1109/tnnls.2020.3016523 Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi Auteurs: Carlo Michaelis, Andrew B. Lehr, Christian Tetzlaff Publié dans: Frontiers in Neurorobotics, Issue 14, 2020, ISSN 1662-5218 Éditeur: Frontiers Research Foundation DOI: 10.3389/fnbot.2020.589532 Hey, look over there: Distraction effects on rapid sequence recall Auteurs: Daniel Miner, Christian Tetzlaff Publié dans: PLOS ONE, Issue 15/4, 2020, Page(s) e0223743, ISSN 1932-6203 Éditeur: Public Library of Science DOI: 10.1371/journal.pone.0223743 A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents Auteurs: Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta Publié dans: Frontiers in Neurorobotics, Issue 11, 2017, ISSN 1662-5218 Éditeur: Frontiers Research Foundation DOI: 10.3389/fnbot.2017.00020 Principles underlying the input-dependent formation and organization of memories Auteurs: Juliane Herpich, Christian Tetzlaff Publié dans: Network Neuroscience, Issue 3/2, 2019, Page(s) 606-634, ISSN 2472-1751 Éditeur: The MIT Press DOI: 10.1162/netn_a_00086 Peri-hand space expands beyond reach in the context of walk-and-reach movements Auteurs: Michael Berger, Peter Neumann, Alexander Gail Publié dans: Scientific Reports, Issue 9/1, 2019, ISSN 2045-2322 Éditeur: Nature Publishing Group DOI: 10.1038/s41598-019-39520-8 Recognition and prediction of manipulation actions using Enriched Semantic Event Chains Auteurs: Fatemeh Ziaeetabar, Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter Publié dans: Robotics and Autonomous Systems, Issue 110, 2018, Page(s) 173-188, ISSN 0921-8890 Éditeur: Elsevier BV DOI: 10.1016/j.robot.2018.10.005 A Fast Online Frequency Adaptation Mechanism for CPG-Based Robot Motion Control Auteurs: Mathias Thor, Poramate Manoonpong Publié dans: IEEE Robotics and Automation Letters, Issue 4/4, 2019, Page(s) 3324-3331, ISSN 2377-3766 Éditeur: IEEE DOI: 10.1109/lra.2019.2926660 A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity Auteurs: Janne Lappalainen, Juliane Herpich, Christian Tetzlaff Publié dans: Frontiers in Computational Neuroscience, Issue 13, 2019, ISSN 1662-5188 Éditeur: Frontiers Research Foundation DOI: 10.3389/fncom.2019.00026 Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data Auteurs: Valentina A. Unakafova, Alexander Gail Publié dans: Frontiers in Neuroinformatics, Issue 13, 2019, ISSN 1662-5196 Éditeur: Frontiers Research Foundation DOI: 10.3389/fninf.2019.00057 Symbol Emergence in Cognitive Developmental Systems: a Survey Auteurs: Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann, Lorenzo Jamone, Takayuki Nagai, Benjamin Rosman, Toshihiko Matsuka, Naoto Iwahashi, Erhan Oztop, Justus Piater, Florentin Worgotter Publié dans: IEEE Transactions on Cognitive and Developmental Systems, 2018, Page(s) 1-1, ISSN 2379-8920 Éditeur: IEEE DOI: 10.1109/tcds.2018.2867772 Error-Based Learning Mechanism for Fast Online Adaptation in Robot Motor Control Auteurs: Mathias Thor, Poramate Manoonpong Publié dans: IEEE Transactions on Neural Networks and Learning Systems, 2019, Page(s) 1-10, ISSN 2162-237X Éditeur: IEEE Computational Intelligence Society DOI: 10.1109/tnnls.2019.2927737 Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency Auteurs: Krisna Rungruangsak-Torrissen, Poramate Manoonpong Publié dans: PLOS ONE, Issue 14/8, 2019, Page(s) e0216030, ISSN 1932-6203 Éditeur: Public Library of Science DOI: 10.1371/journal.pone.0216030 The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity Auteurs: Steffen Krüppel; Christian Tetzlaff Publié dans: Network Neuroscience, Issue 1, 2020, ISSN 2472-1751 Éditeur: MIT Press DOI: 10.1101/612341 Editorial: Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology Auteurs: Poramate Manoonpong, Christian Tetzlaff Publié dans: Frontiers in Neurorobotics, Issue 12, 2018, ISSN 1662-5218 Éditeur: Frontiers Research Foundation DOI: 10.3389/fnbot.2018.00053 General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots Auteurs: Aitor Miguel-Blanco, Poramate Manoonpong Publié dans: Frontiers in Neural Circuits, Issue 14, 2020, ISSN 1662-5110 Éditeur: Frontiers Research Foundation DOI: 10.3389/fncir.2020.00046 Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex Auteurs: Michael Berger, Naubahar Shahryar Agha, Alexander Gail Publié dans: eLife, Issue 9, 2020, ISSN 2050-084X Éditeur: eLife Sciences Publications DOI: 10.7554/elife.51322 Conference proceedings (5) Autobot for Effective Design Space Exploration and Agile Generation of RBFNN Hardware Accelerator in Embedded Real-time Computing Auteurs: Nan-Sheng Huang, Jorgen Christian Larsen, Poramate Manoonpong Publié dans: 2020 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2020, Page(s) 339-344, ISBN 978-1-7281-7293-4 Éditeur: IEEE DOI: 10.1109/rcar49640.2020.9303043 End-to-End Rapid FPGA Prototyping for Embedded Proactive BMI Control Auteurs: Nan-Sheng Huang, Jan-Matthias Braun, Ricardo Rodrigues do Carmo, Jorgen Christian Larsen, Poramate Manoonpong Publié dans: 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020, Page(s) 1-2, ISBN 978-1-7281-7399-3 Éditeur: IEEE DOI: 10.1109/icce-taiwan49838.2020.9258186 Teaching Hardware Implementation of Neural Networks using High-Level Synthesis in Less Than Four Hours for Engineering Education of Intelligent Embedded Computing Auteurs: Nan-Sheng Huang, Jan-Matthias Braun, Jorgen Christian Larsen, Poramate Manoonpong Publié dans: 2019 20th International Carpathian Control Conference (ICCC), 2019, Page(s) 1-7, ISBN 978-1-7281-0702-8 Éditeur: IEEE DOI: 10.1109/carpathiancc.2019.8765994 scalable Echo State Networks hardware generatorfor embedded systems using high-level synthesis Auteurs: Nan-Sheng Huang, Jan-Matthias Braun, Jørgen Christian Larsen, Poramate Manoonpong Publié dans: 8th Mediterranean Conference on Embedded Computing, 2019 Éditeur: IEEE A scalable Echo State Networks hardware generator for embedded systems using high-level synthesis Auteurs: Nan-Sheng Huang, Jan-Matthias Braun, Jorgen Christian Larsen, Poramate Manoonpong Publié dans: 2019 8th Mediterranean Conference on Embedded Computing (MECO), 2019, Page(s) 1-6, ISBN 978-1-7281-1740-9 Éditeur: IEEE DOI: 10.1109/meco.2019.8760065 Book chapters (1) Development of a Real-Time Motor-Imagery-Based EEG Brain-Machine Interface Auteurs: Gal Gorjup, Rok Vrabič, Stoyan Petrov Stoyanov, Morten Østergaard Andersen, Poramate Manoonpong Publié dans: Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part VII, Issue 11307, 2018, Page(s) 610-622, ISBN 978-3-030-04238-7 Éditeur: Springer International Publishing DOI: 10.1007/978-3-030-04239-4_55 Other (4) The Reach Cage environment for wireless neural recordings during structured goal-directed behavior of unrestrained monkeys Auteurs: Michael Berger, Alexander Gail Publié dans: bioRxiv, 2018 Éditeur: Cold Spring Harbor Laboratory DOI: 10.1101/305334 Symbol Emergence in Cognitive Developmental Systems: a Survey Auteurs: Taniguchi, Tadahiro; Ugur, Emre; Hoffmann, Matej; Jamone, Lorenzo; Nagai, Takayuki; Rosman, Benjamin; Matsuka, Toshihiko; Iwahashi, Naoto; Oztop, Erhan; Piater, Justus; Wörgötter, Florentin Publié dans: Issue 1, 2018 Éditeur: arxiv Action Prediction in Humans and Robots Auteurs: Florentin Wörgötter, Fatemeh Ziaeetabar, Stefan Pfeiffer, Osman Kaya, Tomas Kulvicius, Minija Tamosiunaite Publié dans: 2019 Éditeur: arxiv Generation of Paths in a Maze using a Deep Network without Learning Auteurs: Kulvicius, Tomas; Herzog, Sebastian; Tamosiunaite, Minija; Wörgötter, Florentin Publié dans: Issue 7, 2021 Éditeur: arxiv Recherche de données OpenAIRE... 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