Computation and Modeling



Prof. Dr. Denis Burdakov

ETH Zurich, Department of Health Sciences and Technology (D-HEST)

Research focus: Neural algorithms and behaviour

How does the brain solve complex problems? The Burdakov lab studies brain computations that convert sensory context into appropriate actions, appetites, and arousal. Our experiments focus on specific genetically-defined brain cells, but our questions are more general, overlapping with fields such as robotics (what control algorithms are best for performance in an uncertain world? what are their strengths and weaknesses?).  To answer such questions, the lab studies how information is represented by specific neural clusters to sway decisions.

This is achieved by tracking real-time brain network dynamics (using in vivo genetically-targete­­d calcium reporters, electrophysiology) associated with quantified voluntary actions, while manipulating sensory contexts (internal and external body state) and genetically- and temporally-defined elements of neural computations (using optogenetics, chemogenetics). These sensorimotor measurements are interpreted with the help of computational simulations that formally assess the performance of particular sensorimotor algorithms in defined tasks.  By elucidating what different parts of the brain do, how they do it, and what makes them perform well or badly, this work provides fundamental information that can be used for designing better medical treatments for brain disorders.

Topics: Neural basis of behaviour, computation and modelling, molecular and cellular neuroscience, disorders of the nervous systems




Mathew Cook


Dr. Mathew Cook
Institute of Neuroinformatics, University and ETH Zurich


Topic: Computation and Modeling



Tobi Delbruck


Prof. Tobi Delbruck
Institute of Neuroinformatics, University and ETH Zurich

Research Focus: The sensors group at INI led by myself and PD Dr. Shih-Chii Liu develops neuromorphic silicon vison and audio sensors and methods for processing their output. These sensors and processing methods are inspired by the organizing principle of the nervous system. For instance, the brain uses spikes to transmit analog signals over long distances without losing precision by using interspike time intervals and spike coincidence to encode analog information. One of the main sensors is the dynamic vision sensor (DVS). The DVS encodes visual information by transmitting spikes in response to log intensity changes. This way, the sensor achieves very high dynamic range and can help beat the classical latency-power tradeoff suffered by conventional image sensors. We also develop many robots to demonstrate the advantages of the neuromorphic approach.

Keywords: neuromorphic, sensor, vision, eye, retina, cochlea

Topics: Sensory Systems, Computation and Modeling




Elisa Donati


Dr. Elisa Donati, Junior Group Leader
Institute of Neuroinformatics, ETH Zurich and  University of Zurich

Research focus: My research activities are at the interface of neuroscience and neuromorphic engineering. My main goal is to understand how to develop event-based systems able to interface with humans to process in real-time physiological data as inputs. In particular, I am focusing on biomedical applications where the device should be implanted to restore missing biological functions, (e.g., adaptive pacemakers, biomedical devices for neuroprosthetics).

In addition, I am exploring the peripherical nervous system to understand how to apply physiological models to robotic control, to build a fully event-based pipeline.

Keywords: Neuromorphic, biomedical application, biomedical signal processing, electronic circuits, robotics

Topic: Computation and Modeling and Motor Systems

Publications: scholar





Prof. Dr. Csaba Földy
Brain Research Institute, Laboratory of Neural Connectivity, University of Zurich

Research Focus: We are interested in the role of synapses in brain function. Synapses serve as fundamental sites of information transmission between neurons, with different synapses characterized by different qualities of that transmission. Frequently, these qualities are associated with the type of neurons being connected. We reason that if synaptic transmission forms the basis of information processing in the brain, and that synaptic properties can be studied in a cell-type specific manner, we will reach a deeper understanding of the brain’s information processing by performing molecular and computational analyses of synapses, as defined by their connected cell types. In pursuit of this interest, we use electrophysiology, molecular biology, and computational modeling analyses.

Topics: Molecular and Cellular Neuroscience, Computation and Modeling





Prof. Dr. Benjamin F. Grewe

Institute of Neuroinformatics, ETH Zurich

Research Focus: My primary research focus is directed towards understanding the basis of information processing and memory formation in neuronal networks using experimental as well as computational approaches. Currently my research at the INI investigates basic concepts of information processing and memory formation in limbic neuronal networks, using miniaturized cutting-edge imaging techniques to record learning induced changes in neuronal network activity of mice. Aligning with the combined strengths of the INI and in collaboration biomedical and electrical engineering groups my long-term vision is to extract fundamental principles of network-learning from real biological networks and then to reverse engineer their functionality as logical, reproducible algorithms that be implemented in software or directly as electrical circuits. I am convinced that reverse-engineering neural learning algorithms that mimic human thinking will one day change the importance of intelligent technologies in our everyday life

Keywords: systems and computational neuroscience, population coding, neuronal network learning, deep learning, spiking network simulations, calcium imaging, two photon microscopy, miniaturized microscope, freely moving, behavior

Topic: neural basis of behavior, computation and modeling, sensory systems


Publications: pubmed

Richard Hahnloser


Prof. Dr. Richard Hahnloser
Institute of Neuroinformatics, ETH Zurich and University of Zurich

Research Focus: We research sensorimotor and observational learning, birdsong development, neural coding in auditory and motor brain areas, ultrastructure of synaptic networks.  We make use mainly of computational modeling, behavioral methods, electrophysiology, and light- and electron microscopy.

Keywords: imitation behavior, learning

Topics: Computation and Modeling, Neural Basis of Behavior, Motor Systems




Giacomo Indiveri


Prof. Dr. Giacomo Indiveri
Institute of Neuroinformatics, University of Zurich

Research Focus: Our research concerns the analysis and development of computational models, hybrid analog/digital VLSI circuits, and multi-chip event-based systems for implementing real-time distributed neural processing systems, and eventually building Neuromorphic Cognitive Systems (i.e. neuromorphic architectures that can learn and reason about the actions to take, in response to the combinations of external stimuli, internal states, and behavioral objectives).

The neuromorphic cognitive systems we develop are typically real-time behaving systems comprising multi-chip, multi-purpose spiking neural architectures. They are used to validate brain inspired computational paradigms in real-world scenarios, and to develop a new generation of fault-tolerant event-based computing technologies.

Keywords: Neuromorphic, learning, plasticity, attention, electronic circuits

Topic: Computation and Modeling



Vartan Kurtcuoglu


Prof. Dr. Vartan Kurtcuoglu
The Interface Group, Institute of Physiology, University of Zurich

Research Focus: My group’s goal is to address clinical needs through the convergence of engineering, biological and medical research. Within the neuroscience field, we focus on transport processes in the fluid spaces of the brain, namely in the cerebrospinal, interstitial and perivascular fluids. By combining computational techniques with experimental methods, we aim to understand the dynamics of cerebral fluid motion, the driving forces behind these and how they, along with the associated transport processes of metabolites and other substances, are involved in the pathogenesis of CNS disorders

Keywords: volume transmission, fluid dynamics, hydrocephalus, astrocyte network, mechanosensing

Topic: Computation and Modeling, Biomedical Technology and Imaging, Disorders of the Nervous System






Prof. Dr. Nicolas Langer
Methods of Plasticity Research, Department of Psychology, University of Zurich

Research Focus: Our lab develops and obtains new neurophysiological and neuroimaging measures in the context of human brain and behavioral plasticity. Specifically, we investigate the potential for plasticity, mechanisms for stabilization and compensation across the lifespan. In particular, we investigate the relationship between brain plasticity and cognitive functioning, such as perceptual processing, learning, (working-) memory, decision-making and processing speed.
In this context of neuroplasticity research, we are designing and implementing novel multi-modal paradigms (e.g. combined EEG eye-tracking), extracting and associate them with state of the art neuroscientific methods, such as functional network models, machine learning, longitudinal analyses and computational modeling. These paradigms can also be used to decompose the critical component processes underlying performance of the behavioral tests that are used routinely in clinical diagnosis. This multi-level, multi-modal design allows us to study cognitive performance and perception at their desired level of analysis, and to elucidate variations in performance across the continuum from healthy to pathological functioning. To investigate those research aims and objectives, we are using a variety of psychological and neuroscientific methods, such as EEG, eye-tracking, structural MRI & DTI, psychophysiology)
Keywords: EEG, eye-tracking, cognitive modeling, machine-learning, cognition, multi-modal imaging, structural MRI, DTI, development, neurophenotyping, Research Domain Criteria (RDoC).

Topics: Cognitive Neuroscience, Computation and Modeling, Neural Basis of Behavior, Development and Regeneration

Publications: PubMed Google Scholar


Shih-Chii Liu


Prof. Dr. Shih-Chii Liu
Institute of Neuroinformatics (INI), University of Zurich and ETH Zurich
Research Focus: I co-lead the sensors group at INI. Our group develops neuromorphic silicon cochlea and retina sensors and methods for processing their output. My focus is on the design of silicon spiking cochleas such as the AEREAR2 cochlea, and the development of real-time event-driven auditory processing algorithms and networks for tasks such as classification and recognition, and together with the dynamic vision sensor (DVS) in tasks such as sensory fusion tasks. These sensors and processing methods are inspired by the organizing principles of the nervous system. We also look for neural electronic equivalents of these algorithms through implementations in FPGA or in custom silicon, for example, silicon dendritic circuits and in the process, we hope to develop an understanding of some of the principles used in our brains for processing information.

Keywords: neuromorphic sensor, cochlea, networks, deep spiking networks, auditory systems, spike coding,

Topic: Computation and Modeling, Sensory Systems



Jean-Pascal Pfister


Prof. Dr. Jean-Pascal Pfister
Theoretical Neuroscience Group, Institute of Neuroinformatics, University of Zurich and ETH Zurich

Research Focus: One of the most fascinating properties of the brain is its ability to extract relevant information from the environment (e.g. extract the voice of a single person in a crowd, recognize an object when only part of it is observed). However very little is known about how this feature extraction is performed in the brain. I am particularly interested in better understanding how this feature extraction is implemented at the level of neurons and synapses. More generally, I am interested in developing new statistical models and apply them in the field of neuroscience.  This statistical approach in neuroscience is mainly focused at the level of spiking neurons.

Keywords: Bayesian Inference, Learning, Synaptic Plasticity, Spiking Neurons, Statistical Models

Topic: Computation and Modeling





Polania Rafael


Prof. Dr. Rafael Polania
Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich

Research Focus: Humans do not react to the environment in a reflexive manner, but can freely choose which action to perform in response to a given situation. The neural processes that enable such flexible decision making are fundamental components of human cognition and have attracted a lot of interest from researchers in many scientific disciplines such as neuroscience, psychology, economics, and medicine.

The research agenda at the Decision Neuroscience Lab bridges these multiple disciplines across theoretical and empirical domains to establish important links between the computational, psychological and neural processes controlling human decision making, by providing both correlative and causal evidence that well-defined neural signals are indeed driving both computationally defined cognitive processes and the resulting behavior. This research thus has the potential to unite conceptually separate approaches to the study of distinct types of human behavior and thereby contribute information that is crucial for the diagnosis and treatment of psychiatric and neurological disorders involving decision-making pathologies (e.g. ADHD, obesity, addiction).

Topics: Cognitive Neuroscience, Computation and Modeling, Neural Basis of Behavior

Keywords: decision-making, EEG, fMRI, non-invasive brain stimulation, reward, perception, economics.

Publications: google-scholar



Dr. Yulia Sandamirskaya, Junior Group Leader
Institute of Neuroinformatics, University and ETH Zurich

Research Focus: I work on development of embodied cognitive systems using neuromorphic technology and conceptual and mathematical framework of Dynamic Neural Fields. Dynamic Neural Fields describe dynamics of large homogeneous neuronal populations, can be used to account for psychophysics of cognitive abilities (such as, e.g., working memory, perceptual processes, learning, or executive control) and their development, and can be used to control cognitive robots. We implement the developed cognitive neuromorphic architectures on robotic agents that autonomously learn to represent their environment and plan goal-directed action sequences, demonstrating how cognitive representations and intelligent behaviour may emerge in an interaction between the neuronal system, the agent’s body, and the surrounding environment. 

Keywords: Dynamic Neural Fields, neuromorphic cognitive architectures, sequence generation, concept formation, autonomous learning, cognitive neuro-robotics.

Topics: Computation and Modeling




Schmid Daners Marianne


Dr. Marianne Schmid Daners

Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich

Research Focus: At the interdisciplinary interface of clinical research and engineering my research focuses on the modelling, control and testing of biomedical systems as well as on the development and control of devices for the treatment of hydrocephalus. One particular focus is to gain fundamental insights into the physiologic dynamics within and adjacent to the cerebrospinal fluid spaces and to develop a pathologic hydrocephalus model. In addition, my research on the cardiovascular system contributes to the understanding of intracranial and spinal dynamics and may support further work on brain perfusion.

Keywords: pressure interaction, testing, sensors, physiologic control, gait analysis, hydrocephalus

Topic: Computation and Modeling, Biomedical Technology and Imaging, Disorders of the Nervous System

Publications: Web of Science Researcher ID E-1800-2013



Klaas E. Stephan


Prof. Dr. Klaas E. Stephan
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, UZH / ETH


Topic: Computation and Modeling