Cognitive systems; Neurorobotics; Embodied Cognition; Neuromorphic Engineering; Neural dynamics
Baumgartner Sandro, Renner Alpha, Kreiser Raphaela, Liang Dongchen, Indiveri Giacomo, Sandamirskaya Yulia (2020), Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach, in 2020 IEEE International Symposium on Circuits and Systems (ISCAS)
, SevillaIEEE, Virtual.
Asgari Hajar, Maybodi Babak Mazloom-Nezhad, Kreiser Raphaela, Sandamirskaya Yulia (2020), A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task, in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
, Genova, ItalyIEEE, Genova, Italy.
Stagsted Rasmus, Vitale Antonio, Binz Jonas, Renner Alpha, Bonde Larsen Leon, Sandamirskaya Yulia (2020), Towards neuromorphic control: A spiking neural network based PID controller for UAV, in Robotics: Science and Systems 2020
, Robotics: Science and Systems Foundation, Virtual.
Kreiser Raphaela, Renner Alpha, Leite Vanessa R. C., Serhan Baris, Bartolozzi Chiara, Glover Arren, Sandamirskaya Yulia (2020), An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot, in Frontiers in Neuroscience
, 14, 551.
Kreiser Raphaela, Waibel Gabriel, Armengol Nuria, Renner Alpha, Sandamirskaya Yulia (2020), Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware, in 2020 IEEE International Conference on Robotics and Automation (ICRA)
, Paris, FranceIEEE, Paris, France.
Liang Dongchen, Kreiser Raphaela, Nielsen Carsten, Qiao Ning, Sandamirskaya Yulia, Indiveri Giacomo (2019), Neural State Machines for Robust Learning and Control of Neuromorphic Agents, in IEEE Journal on Emerging and Selected Topics in Circuits and Systems
, 9(4), 679-689.
Tekülve Jan, Fois Adrien, Sandamirskaya Yulia, Schöner Gregor (2019), Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement, in Frontiers in Neurorobotics
, 13, 95.
Liang Dongchen, Kreiser Raphaela, Nielsen Carsten, Qiao Ning, Sandamirskaya Yulia, Indiveri Giacomo (2019), Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents, in 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
, Hsinchu, TaiwanIEEE, Hsinchu, Taiwan.
RennerAlpha, EvanusaMatthew, SandamirskayaYulia (2019), Event-Based Attention and Tracking on Neuromorphic Hardware, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
, Long Beach, CAIEEE Xplore, online.
LiangDongcheng, KreiserRaphaela, SandamirskayaYulia, IndiveriGiacomo (2019), Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents, in 1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
, Hsinchu, TaiwanIEEE, online.
Duran Boris, Sandamirskaya Yulia (2018), Learning Temporal Intervals in Neural Dynamics, in IEEE Transactions on Cognitive and Developmental Systems
, 10(2), 359-372.
Kreiser Raphaela, Cartiglia Matteo, Martel Julien N.P., Conradt Jorg, Sandamirskaya Yulia (2018), A Neuromorphic Approach to Path Integration: A Head-Direction Spiking Neural Network with Vision-driven Reset, in 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
, Florence, ItalyIEEE, IEEE Xplore.
Martel Julien N. P., Muller Jonathan, Conradt Jorg, Sandamirskaya Yulia (2018), An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors, in 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
, Florence, ItalyIEEE, IEEE Xplore.
Martel Julien N. P., Muller Lorenz K., Carey Stephen J., Muller Jonathan, Sandamirskaya Yulia, Dudek Piotr (2018), Real-Time Depth From Focus on a Programmable Focal Plane Processor, in IEEE Transactions on Circuits and Systems I: Regular Papers
, 65(3), 925-934.
Lowe Robert, Sandamirskaya Yulia (2018), Learning and adaptation: neural and behavioural mechanisms behind behaviour change, in Connection Science
, 30(1), 1-4.
Strub Claudius, Schöner Gregor, Wörgötter Florentin, Sandamirskaya Yulia (2017), Dynamic Neural Fields with Intrinsic Plasticity, in Frontiers in Computational Neuroscience
, 11, 74.
GlatzSebastian, MartelJulien N. P., KreiserRaphaela, QiaoNing, SandamirskayaYulia, Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor, in IEEE International Conference on Robotcs and Automation (ICRA)
, IEEE Xplore, Online.
IndiveriGiacomo, SandamirskayaYulia, The importance of space and time for signal processing in neuromorphic agents, in IEEE Signal Processing Magazine
The goal of this project is to enhance a new type of intelligent sensorimotor systems that are currently being developed by the applicant -- the neuromorphic embodied cognitive systems -- with long-term memory. This goal has a theoretical and a technological components. In the theory part, the mechanisms of formation of long-term memories in neuronal systems will be realised in computational architectures that are embodied, i.e. receive inputs from sensors and can generate actions in an environment. Such long-term memories form the basis of cognition and intelligent behavior by storing representations of objects, actions, episodes, and temporal sequences. The technological goal of the project is to create cognitive neuromorphic robotic agents that can act and perceive in the real world and build representations in an interactive process with the environment. The project aims to enable interaction of the theory- and technology-driven research. By creating a technology for realisation of neuronal cognitive architectures in robotic agents, the project will enable testing models and generating new hypothesis for neuroscience experiments in a closed-loop behavioral setting. On the other hand, the theoretical insights gained when developing computational neuronal models capable to generate long-term memory for objects, actions, and spatio-temporal relations between them can drive development of new algorithms for solving tasks that involve sensorimotor interactions and require adaptivity and learning in new environments. To achieve this goal, I will use the computational framework of attractor dynamics. This neural-dynamic framework has been used in the past both to account for activity of neuronal populations in form of Dynamic Neural Fields (DNF) models and to explain the sensorimotor and developmental basis of cognitive behavior in the embodied cognition paradigm. In this project, the DNF framework will be extended with architectures for forming and storing long-term memories, developed in collaboration with experimental neuroscientists, who probe formation of episodic and sequential memories in rodents and formation of representations of songs in birds. The modelling work will be based on a body of work on embodied cognition, computational neuroscience, artificial intelligence, and cognitive robotics. The obtained computational models will be realised in spiking neuromorphic VLSI technology, which is low-power and efficient for implementing neuronal architectures. These neuromorphic cognitive hardware will be interfaced to sensors and motors of robotic vehicles, which will generate action sequences and form long-term memories of the observed environmental contingencies. Adaptivity, learning, and long-term memory formation of the cognitive robotic agents will be probed in benchmark scenarios. This project will play a key role in moving neuromorphic devices towards application in real-world scenarios: servailliance, robotics, and smart environments and will have an impact in the research fields of machine learning, cognitive robotics, and computational neuroscience.