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ECogNeT

Embodied Cognitive Neuromorphic Technology

Total Cost €

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EC-Contrib. €

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Partnership

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Project "ECogNeT" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITAT ZURICH 

Organization address
address: RAMISTRASSE 71
city: ZURICH
postcode: 8006
website: n.a.

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 Switzerland [CH]
 Project website https://www.neuromorphicrobots.com
 Total cost 187˙419 €
 EC max contribution 187˙419 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-04-01   to  2018-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITAT ZURICH CH (ZURICH) coordinator 187˙419.00

Map

 Project objective

Biological neuronal networks allow humans and animals to build representations of objects, complex scenes, and temporal sequences and to use these representations to plan and execute goal-directed actions, learn new skills, and constantly adapt to changing situations. Neuronal systems have evolved to be extremely powerful in perceiving and acting in complex and dynamic real-world environments. The field of neuromorphic engineering is realising neuronal computation in a new generation of hardware systems, aiming to enable computation with high efficiency and low energy cost, comparable to biological neuronal networks. In order to use this hardware to implement systems that can solve perceptual, motor, and cognitive tasks, cognitive architectures have to be developed, which integrate elementary cognitive processes in a coherent computational framework. The ECogNeT project aims to develop such a computational framework by realising neural-dynamic models of embodied cognition in neuromorphic hardware. In particular, the computational and conceptual framework of Dynamic Neural Fields will be used to create cognitive computational primitives in neuromorphic hardware, which will organise the hardware neurons in attractor-networks, capable to generate discrete, cognitive representations from sensory inputs. The developed neuromorphic cognitive architecture will be integrated with sensors and motors of a robotic agent and its capability to represent environmental situations and temporal sequences of events will be validated in benchmark scenarios, exploiting the potential of this new technology in development of novel, low-power, fast, and smart devices capable to work in real-world settings in order to advance future prosthetic systems, smart environments, and assistive robots.

 Publications

year authors and title journal last update
List of publications.
2017 Milde, M.; Dietmüller, A.; Blum, H.; Indiveri, G. & Sandamirskaya, Y.
Obstacle avoidance and target acquisition in mobile robots equipped with neuromorphic sensory-processing systems
published pages: , ISSN: , DOI:
IEEE Synposium for Circuits and Systems, ISCAS 2019-07-26
2017 C. Strub, G. Schöner, F. Wörgötter, Y. Sandamirskaya
Dynamic Neural Fields with Intrinsic Plasticity
published pages: , ISSN: 1663-4365, DOI:
Frontiers in Computational Neuroscience in press 2019-07-26
2017 Milde, M. B.; Blum, H.; Dietmüller, A.; Sumislawska, D.; Conradt, J.; Indiveri, G. & Sandamirskaya, Y.
Obstacle avoidance and target acquisition for robot navigation using a mixed signal analog/digital neuromorphic processing system
published pages: , ISSN: 1662-5218, DOI:
Frontiers in Neurorobotics 2019-07-26
2017 Salt, L.; Indiveri, G. & Sandamirskaya, Y.
Obstacle avoidance with LGMD neuron: towards a neuromorphic UAV implementation
published pages: , ISSN: , DOI:
IEEE International Symposium on Circuits and Systems (ISCAS) 2019-07-26
2017 Blum, H.; Dietmüller, A.; Milde, M.; Conradt, J.; Indiveri, G. & Sandamirskaya, Y.
A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor
published pages: , ISSN: , DOI:
Robotics Science and Systems Conference, RSS 2019-07-26
2017 Duran, B. & Sandamirskaya, Y.
Learning Temporal Intervals in Neural Dynamics
published pages: , ISSN: 2379-8939, DOI:
IEEE Transactions on Cognitive and Developmental Systems 2019-07-26

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The information about "ECOGNET" are provided by the European Opendata Portal: CORDIS opendata.

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