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Predictive Neural Information for Proactive Actions: From Monkey Brain to Smart House Control

Total Cost €


EC-Contrib. €






Project "Plan4Act" data sheet

The following table provides information about the project.


Organization address
postcode: 37073

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website
 Total cost 4˙236˙000 €
 EC max contribution 4˙236˙000 € (100%)
 Programme 1. H2020-EU.1.2.2. (FET Proactive)
 Code Call FETPROACT-2016
 Funding Scheme RIA
 Starting year 2017
 Duration (year-month-day) from 2017-01-01   to  2020-12-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
3    SYDDANSK UNIVERSITET DK (ODENSE M) participant 924˙625.00
5    MYSPHERA SL ES (PATERNA) participant 387˙000.00


 Project objective

Planning and mental simulation of actions and outcomes are a major cognitive trait of humans. We predict action consequences and perform goal-directed actions in proactive, forward-looking ways. By contrast, systems that lack predictive planning are reactive and dominated by reflex-like, cumbersome behaviors. Most currently existing brain-machine-interfaces (BMI) fall into this category. Plan4Act sets out to go beyond this by inferring actions from action-predicting neural activity of complex action sequences. Neurophysiology in non-human primates recently revealed that such encoding is far more widespread than previously thought. The goal of the Plan4Act project is to record and understand predictive neural activity and use it to proactively control devices in a smart house. The far-future vision behind this is to endow motor-impaired patients with the ability to plan a daily-life goal – like making coffee – and achieve it without having to invoke one by one every single individual action to reach this goal. To approach this complex problem, we record multi-unit action predicting activity in macaques (WP1), model this by adaptive neural networks (WP2), design therefrom an embedded (FPGA-based) controller (WP3), and interface it with a smart house (WP4) to control action sequences with a clear look-ahead property. The main outcome of this project is a system that integrates the above components at TRL4 for which we quantify improved reaction speed and robustness of this type of proactive BMI control. The understanding and use of predictive neural signals for machine control is novel and methods, algorithms, and hardware developed to translate predictive planning from neural activity to technology create the major general impact of this project. Potential translational and commercial interests will be assessed by our industrial partner, where specifically the embedded controller and its smart house interface are expected to create near-future commercial impact, too.


List of deliverables.
Short report and specification data sheet about the implementation of the interfaces for the Smart House. Documents, reports 2019-11-22 11:57:37
Data sheet of definitions of experimental conditions for the Smart House as depending on the setup and data of WP1. Documents, reports 2019-11-22 11:57:33
First Report on status of experimental setup (SmartCage), training, behavioral testing, and neural recording. Documents, reports 2019-11-22 11:57:34
Demonstration of Smart House control using the software controller from WP2 and the Test 1 condition from WP1. Demonstrators, pilots, prototypes 2019-11-22 11:57:34
Report on behavioral testing and status of recording with SmartCage action sequence planning for Test 1. Documents, reports 2019-11-22 11:57:40
Report on generic reduced control units for complex action sequence formation. Documents, reports 2019-11-22 11:57:47
Report on neural network model using the sequential structure to predict the present sequence. Documents, reports 2019-11-22 11:57:36
Report on neural network model encoding sequences. Documents, reports 2019-08-30 13:23:47
Report on the status of Smart House devices and interfaces for connecting to the controllers of WP2 and WP3. Documents, reports 2019-08-30 13:23:47
Website Websites, patent fillings, videos etc. 2019-08-30 13:23:47
Dissemination strategy and plan. Documents, reports 2019-08-30 13:23:47
Data Management Plan Open Research Data Pilot 2019-08-30 13:23:47

Take a look to the deliverables list in detail:  detailed list of Plan4Act deliverables.


year authors and title journal last update
List of publications.
2019 Valentina A. Unakafova, Alexander Gail
Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
published pages: , ISSN: 1662-5196, DOI: 10.3389/fninf.2019.00057
Frontiers in Neuroinformatics 13 2019-09-05
2019 Michael Berger, Peter Neumann, Alexander Gail
Peri-hand space expands beyond reach in the context of walk-and-reach movements
published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-019-39520-8
Scientific Reports 9/1 2019-09-05
2017 Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents
published pages: , ISSN: 1662-5218, DOI: 10.3389/fnbot.2017.00020
Frontiers in Neurorobotics 11 2019-08-30
2018 Fatemeh Ziaeetabar, Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter
Recognition and prediction of manipulation actions using Enriched Semantic Event Chains
published pages: 173-188, ISSN: 0921-8890, DOI: 10.1016/j.robot.2018.10.005
Robotics and Autonomous Systems 110 2019-09-05
2019 Juliane Herpich, Christian Tetzlaff
Principles underlying the input-dependent formation and organization of memories
published pages: 606-634, ISSN: 2472-1751, DOI: 10.1162/netn_a_00086
Network Neuroscience 3/2 2019-09-05
2019 Mathias Thor, Poramate Manoonpong
Error-Based Learning Mechanism for Fast Online Adaptation in Robot Motor Control
published pages: 1-10, ISSN: 2162-237X, DOI: 10.1109/tnnls.2019.2927737
IEEE Transactions on Neural Networks and Learning Systems 2019-09-05
2019 Florentin Wörgötter, Fatemeh Ziaeetabar, Stefan Pfeiffer, Osman Kaya, Tomas Kulvicius, Minija Tamosiunaite
Action Prediction in Humans and Robots
published pages: , ISSN: , DOI:
2019 Nan-Sheng Huang, Jan-Matthias Braun, Jørgen Christian Larsen, Poramate Manoonpong
scalable Echo State Networks hardware generatorfor embedded systems using high-level synthesis
published pages: , ISSN: , DOI:
8th Mediterranean Conference on Embedded Computing 2019-09-05
2018 Michael Berger, Alexander Gail
The Reach Cage environment for wireless neural recordings during structured goal-directed behavior of unrestrained monkeys
published pages: , ISSN: , DOI: 10.1101/305334
bioRxiv 2019-09-05
2018 Taniguchi, Tadahiro; Ugur, Emre; Hoffmann, Matej; Jamone, Lorenzo; Nagai, Takayuki; Rosman, Benjamin; Matsuka, Toshihiko; Iwahashi, Naoto; Oztop, Erhan; Piater, Justus; Wörgötter, Florentin
Symbol Emergence in Cognitive Developmental Systems: a Survey
published pages: , ISSN: , DOI:
1 2019-09-05
2018 Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann, Lorenzo Jamone, Takayuki Nagai, Benjamin Rosman, Toshihiko Matsuka, Naoto Iwahashi, Erhan Oztop, Justus Piater, Florentin Worgotter
Symbol Emergence in Cognitive Developmental Systems: a Survey
published pages: 1-1, ISSN: 2379-8920, DOI: 10.1109/tcds.2018.2867772
IEEE Transactions on Cognitive and Developmental Systems 2019-09-05
2019 Mathias Thor, Poramate Manoonpong
A Fast Online Frequency Adaptation Mechanism for CPG-Based Robot Motion Control
published pages: 3324-3331, ISSN: 2377-3766, DOI: 10.1109/lra.2019.2926660
IEEE Robotics and Automation Letters 4/4 2019-09-05
2019 Janne Lappalainen, Juliane Herpich, Christian Tetzlaff
A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity
published pages: , ISSN: 1662-5188, DOI: 10.3389/fncom.2019.00026
Frontiers in Computational Neuroscience 13 2019-09-05

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