RESULTS

The project milestones are

DELIVERABLES

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

Deliverable D1.3 Report on neural encoding of complex action sequences in SmartCage for Test 2 and update on 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

Deliverable D2.3 Report on neural network model enabling the prediction of complex planned sequences of actions

Deliverable D2.4 Report on neural network model which predicts distractorrobustly the planned sequence of actions

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

Deliverable D3.2 Report on the adaptable network controller for complex action sequence generation and smart house control

Deliverable D3.3 Final report on the hardware controller and integration into the demonstrator for proactive neural information‑based smart house control

ARTICLES

Article in MDPI journal

May 2021, UPM published the work “A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation” Abstract Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still […]

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Article in SpringerLink Behavior Research Methods

April, 2021 DPZ published the work “The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior“ Abstract Ongoing goal-directed movements can be rapidly adjusted following new environmental information, e.g., when chasing pray or foraging. This makes movement trajectories in go-before-you-know decision-making a suitable behavioral readout of the ongoing decision process. Yet, […]

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Article in Nature communication biology journal

March, 2021, UGOE published the work “Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks” Abstract The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic […]

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Article in Frontiers NeuroRobotics Journal

November, 2020 UGOE published the work “Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi“ Abstract Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios […]

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Article in Frontiers Neural Circuits Journal

October, 2020 UGOE published the work “The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations“ Abstract It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens […]

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Article Published in 2020 IEEE International Conference on Real-time Computing and Robotics (RCAR)

September, 2020 SDU published the work “Autobot for Effective Design Space Exploration and Agile Generation of RBFNN Hardware Accelerator in Embedded Real-time Computing“ Abstract This paper presents a method of employing Auto-bot to replace humans in the task of efficient hardware design for radial basis function neural network (RBFNN) in real-time computing applications. Autobot applies […]

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Article in 2020 IEEE International Conference on Consumer Electronics – Taiwan (ICCE-Taiwan)

September, 2020 SDU published the work “End-to-End Rapid FPGA Prototyping for Embedded Proactive BMI Control“ Abstract This paper presents an end-to-end rapid prototyping methodology that performs automated and efficient mapping of desired neural networks onto FPGA. The design automation agent is considered as Autobot. An early prototype with the hardware decoder generated on the FPGA […]

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Article in Frontiers Neural Circuits Journal

August, 2020 SDU published the work “General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots“ Abstract Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is […]

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Article in Nature Scientific Reports

February, 28 2019 DPZ published the work “Peri-hand space expands beyond reach in the context of walk-and-reach movements“ Abstract The brain incorporates sensory information across modalities to be able to interact with our environment. The peripersonal space (PPS), defined by a high level of crossmodal interaction, is centered on the relevant body part, e.g. the […]

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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 Neuroscience

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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Article in Nature Scientific reports Journal

March, 2020 UGOE published the work “Humans Predict Action using Grammar-like Structures“ Abstract Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static […]

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Article in ScienceDirect Neural Networks

March, 2020 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 the […]

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Article in PLOS ONE

August, 28 2019 SDU published the work “Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency“ Abstract The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day–1). It is developed using recurrent neural networks of reservoir computing type, for […]

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Article in Frontiers Neurorobotic Journal

October, 3 2019 UGOE published the work “Embodied Synaptic Plasticity With Online Reinforcement Learning“ Abstract The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body […]

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Article Published in IEEE Robotics and Automation Letters Vol.4

October, 2019 SDU published the work “A Fast Online Frequency Adaptation Mechanism for CPG-Based Robot Motion Control“ Abstract In this letter, we present an online learning mechanism called the dual integral learner for fast frequency adaptation in neural central pattern generator (CPG) based locomotion control of a hexapod robot. The mechanism works by modulating the […]

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Article in Network neuroscience

May, 1 2019 UGOE published the work “Principles underlying the input-dependent formation and organization of memories “ Abstract The neuronal system exhibits the remarkable ability to dynamically store and organize incoming information into a web of memory representations (items), which is essential for the generation of complex behaviors. Central to memory function is that such memory […]

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Article Published in International Conference on Neural Information Processing (ICONIP 2018)

November, 2018 SDU published the work “Development of a Real-Time Motor-Imagery-Based EEG Brain-Machine Interface“ Abstract EEG-based brain-machine interfaces offer an alternative means of interaction with the environment relying solely on interpreting brain activity. They can not only significantly improve the life quality of people with neuromuscular disabilities, but also present a wide range of opportunities […]

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Article in Frontiers Computational Neuroscience Journal

May, 8 2019 UGOE team published the original research article “A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity“ Abstract Synaptic plasticity serves as an essential mechanism underlying cognitive processes as learning and memory. For a better understanding detailed theoretical models combine experimental underpinnings of synaptic plasticity and match experimental results. However, […]

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Article in Frontiers Neuroinformatics journal

July 30, 2019, DPZ published the work “Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data” Abstract Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we […]

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Article Published in 2019 8th Mediterranean Conference on Embedded Computing (MECO)

June, 2019 SDU published the work “A scalable Echo State Networks hardware generator for embedded systems using high-level synthesis“ Abstract Reservoir computing (RC) features with the rich computational dynamics is a kind of powerful machine learning paradigm that is well suited for non-linear time-series prediction and classification problems. However, this impressive performance comes with a […]

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Article Published in: 2019 20th International Carpathian Control Conference (ICCC)

May, 2019 SDU published the work “Teaching Hardware Implementation of Neural Networks using High-Level Synthesis in Less Than Four Hours for Engineering Education of Intelligent Embedded Computing“ Abstract This paper presents the motivation and design of a mini-course to teach hardware implementation of neural networks using high-level synthesis (HLS) in less than four hours for […]

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Article Published in 2019 8th Mediterranean Conference on Embedded Computing (MECO)

June, 2019 SDU published the work “A scalable Echo State Networks hardware generator for embedded systems using high-level synthesis“ Abstract Reservoir computing (RC) features with the rich computational dynamics is a kind of powerful machine learning paradigm that is well suited for non-linear time-series prediction and classification problems. However, this impressive performance comes with a […]

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Article Published in IEEE Transactions on Cognitive and Developmental Systems ( Volume: 11, Issue: 4, Dec. 2019)

August, 2018 UGOE published the work “Symbol Emergence in Cognitive Developmental Systems: A Survey“ Abstract Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human […]

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Article in Frontiers Neurorobotics journal

August 2018, UGOA and SDU published the work “Editorial: Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology” Abstract The brain of biological organisms is a highly complex and very efficient computing unit. It can deal with a multitude of tasks from low-level sensorimotor coordination to high-level cognition. […]

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Article in Frontiers Neurorobotics journal

April 2017, SDU published the work “A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents” Abstract Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a […]

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