The project milestones are



RESEARCH TOPIC: Neural Recording

Different areas of the brain are involved in planning future movements. We register detailed neural planning or predictive activity while monkeys perform instructed sequences of actions in a Smart Cage environment. As the animal moves around freely, we
wirelessly transmit neural signals corresponding to the animal’s plans and actions. We extract animal’s plans and actions. We
extract signatures of proactive action planning from behaviour and brain activity.

Deliverable D1.1 Wireless neural recordings

Deliverable D1.2 Report on behavioral testing and status of recording with SmartCage action sequence planning for Test 1


RESEARCH TOPIC: Neural Modeling

The analysis of the data from neural recordings as well as the development of the adaptive neural controller are supported by a
new theoretical model. This theoretical model links the formation and execution of complex action sequences with the underlying
neuronal and synaptic dynamics enabling the extrapolation of new, synthetic data given various simulated scenarios.

Deliverable D2.1 Neural network model encoding sequences by clustered connectivity

Deliverable D2.2 Report on neural network model using the sequential structure to predict the present sequence


RESEARCH TOPIC: Generic FPGA-based adaptive neural controller

The developed controller will directly interface to the neuralrecording system and the smart house system. It will process
recorded sequence-predicting neural activity, predict the upcoming sequence of actions, and generate the corresponding
complex action sequences to manipulate the smart house.

Deliverable D3.1 Report on generic reduced control units for complex action sequence formation


RESEARCH TOPIC: SmartHouse Interface & control

Smart home devices are orquestrated by IoT platform enabling users to control actuators such as doors, lights, air conditioning
and even subscribed smart devices by simple commands. These commands are implemented through a Gateway which will
authenticate and interpret orders from the neural controller, in order to provide simple and machine-understandable instructions.

Deliverable D4.1 Report on the status of SmartHouse devices and interfaces for connecting to the controllers of WP2 and WP3

Deliverable D4.2 Data sheet of definitions of experimental conditions for the Smart House as depending on the setup and data of WP1

Deliverable D4.3 Short report and specification data sheet about the implementation of the interfaces for the Smart House

Deliverable D4.4 Demonstration of Smart House control using the software controller from WP2 and the Test 1 condition from WP1


Data Management & Dissemination Strategy

Deliverable D6.2 Data Management Plan

Deliverable D6.3 Dissemination Strategy and Plan


Article in Science Direct Journal

December, 14 2019 UGOE published the work “Evolving artificial neural networks with feedback“ Abstract Neural networks in the brain are dominated by sometimes more than 60% feedback connections, which most often have small synaptic weights. Different from this, little is known how to introduce feedback into artificial neural networks. Here we use transfer entropy in […]

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Article in the MIT Press Journal

March 02, 2020 UGOE published the work “The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity“ Abstract Along sensory pathways, representations of environmental stimuli become increasingly sparse and expanded. If additionally the feed-forward synaptic weights are structured according to the inherent organization of stimuli, the increase in sparseness and expansion leads to […]

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Article in PLOS ONE Open Access

April 10, 2020, UGOE published the work “Hey, look over there: Distraction effects on rapid sequence recall” Abstract In the course of everyday life, the brain must store and recall a huge variety of representations of stimuli which are presented in an ordered or sequential way. The processes by which the ordering of these various […]

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Article in the Special Issue Performance, Power and Energy-Efficiency Optimization in Computer Architectures

May 2020, MDPI published the work of the SDU group entitled “AHEAD: Automatic Holistic Energy-Aware Design Methodology for MLP Neural Network Hardware Generation in Proactive BMI Edge Devices” Abstract The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of […]

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Article in eLife

May, 4 2020 DPZ team published the work “Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex“ Abstract System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) […]

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Plan4Act in the Press

Appart from the press article, Alex Gail and Florentin Wörgötter gave an interview to the NDR radio station of Lower Saxony in a radio magazine.

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