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Neural Processing of Distinct Prediction Errors: Theory, Mechanisms & Interventions

Applicant Helmchen Fritjof
Number 180316
Funding scheme Sinergia
Research institution Abteilung Neurophysiologie Institut für Hirnforschung Universität Zürich
Institution of higher education University of Zurich - ZH
Main discipline Interdisciplinary
Start/End 01.09.2018 - 31.08.2022
Approved amount 3'139'320.00
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All Disciplines (5)

Neurophysiology and Brain Research
Electrical Engineering

Keywords (7)

neocortex; neuromorphic; learning rule; mushroom body; dendrites; error propagation; microcircuit

Lay Summary (German)

Eine wesentliche Grundfunktion des Gehirns ist es, sich an neue Herausforderungen in der Umwelt anzupassen und neues Verhalten zu erlernen. 'Lernen aus Fehlern' ist dabei entscheidend. Dennoch ist es weitgehend unverstanden wie Nervenzellen im Verbund der komplexen neuronalen Netzwerke falsche Erwartungen und fehlerhafte Signalverarbeitung korrigieren. In diesem Projekt streben wir ein besseres Verständnis der grundlegenden Mechanismen der Fehlerverarbeitung in Gehirnen an.
Lay summary

Fehler in der Signalverarbeitung im Gehirn können entweder auf einer unzureichenden Interpretation von Sinneseindrücken beruhen oder auf falschen Erwartungen, die das Gehirn an die Aussenwelt hat. Unserem Projekt liegt die Hypothese zugrunde, dass diese beiden Arten von Fehlern in den neuronalen Schaltkreisen unterschiedlich verarbeitet werden und dass dabei verschiedene Kompartimente der Nervenzellen bzw. unterschiedliche Nervenzelltypen beteiligt sind. Basierend auf theoretischen Konzepten der Fehlerverarbeitung beim Lernen werden wir den zugrundeliegenden zellulären Mechanismen sowohl im Mausgehirn als auch im Insektengehirn nachforschen. Die gewonnenen Erkenntnisse sollen wiederum in neuartigen elektronischen Schaltkreisen angewendet werden, sogenannten neuromorphen Chips. Unsere Resultate sollen dazu beitragen, Elektronik mit verbesserter Lernfähigkeit (und sehr geringem Energieverbrauch) zu entwickeln, die sich an die aus der Neurobiologie erkannten Prinzipien der Fehlerverarbeitung anlehnen.  

Direct link to Lay Summary Last update: 13.06.2018

Responsible applicant and co-applicants


Project partner


Efficient Low-Pass Dendro-Somatic Coupling in the Apical Dendrite of Layer 5 Pyramidal Neurons in the Anterior Cingulate Cortex
Marti Mengual Ulisses, Wybo Willem A.M., Spierenburg Lotte J.E., Santello Mirko, Senn Walter, Nevian Thomas (2020), Efficient Low-Pass Dendro-Somatic Coupling in the Apical Dendrite of Layer 5 Pyramidal Neurons in the Anterior Cingulate Cortex, in The Journal of Neuroscience, 40(46), 8799-8815.
Sensory and Behavioral Components of Neocortical Signal Flow in Discrimination Tasks with Short-Term Memory
Gallero-Salas Yasir, Han Shuting, Sych Yaroslav, Voigt Fabian F., Laurenczy Balazs, Gilad Ariel, Helmchen Fritjof (2020), Sensory and Behavioral Components of Neocortical Signal Flow in Discrimination Tasks with Short-Term Memory, in Neuron, 109, 1-14.
Value-guided remapping of sensory cortex by lateral orbitofrontal cortex
Banerjee Abhishek, Parente Giuseppe, Teutsch Jasper, Lewis Christopher, Voigt Fabian F., Helmchen Fritjof (2020), Value-guided remapping of sensory cortex by lateral orbitofrontal cortex, in Nature, 585(7824), 245-250.
Event-Based Computation for Touch Localization Based on Precise Spike Timing
Haessig Germain, Milde Moritz B., Aceituno Pau Vilimelis, Oubari Omar, Knight James C., van Schaik André, Benosman Ryad B., Indiveri Giacomo (2020), Event-Based Computation for Touch Localization Based on Precise Spike Timing, in Frontiers in Neuroscience, 14(420), 1-19.


Group / person Country
Types of collaboration
Seth Tomchik, Scripps Research Neuroscience United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
"Day of Cognition" Talk given at a conference not applicable 06.10.2020 Fribourg, Switzerland Kaldun Jenifer Catherine; Sprecher Simon;
FENS Forum 2020 (virtual) Poster Multi-plane calcium imaging of dendritic activity in specific L5 projections neurons of mouse barrel cortex in a tactile discrimination task. 11.07.2020 virtual meeting (originally Glasgow), Switzerland Schönfeld Gwendolin Claire; Helmchen Fritjof;
FENS Forum 2020 (virtual) Poster Calibrated spike inference from calcium imaging data: generalization across datasets, imaging rates and noise levels. 11.07.2020 virtual meeting (originally Glasgow), Switzerland Helmchen Fritjof; Rupprecht Peter;
Society for Neuroscience Annual Meeting Poster Distinct local and long-range cortical dynamics in an adaptive decision-making task 19.10.2019 Chicago, United States of America Banerjee Abhishek; Helmchen Fritjof; Banerjee Abhishek;

Communication with the public

Communication Title Media Place Year
Media relations: print media, online media Reprogramming Brain Cells Enables Flexible Decision-Making University of Zurich, Website, News German-speaking Switzerland International 2020

Associated projects

Number Title Start Funding scheme
156863 Prospective coding with pyramidal neurons 01.12.2015 Project funding
149858 Imaging cortico-cortical communication in mouse neocortex during behaviour 01.01.2014 Project funding
160756 Hybrid CMOS/Memristive Neuromorphic Systems for Data Analytics 01.09.2015 Sinergia
170269 Inter-areal Neocortical Dynamics during Mouse Behaviour 01.01.2017 Project funding
147485 Barrel Cortex Function (FOR 1341) 01.04.2013 Project funding
186999 Spiking Memristive Architectures for Learning to Learn 01.01.2020 CHIST-ERA
204651 A neuromorphic device to monitor epileptogenicity in the intracranial EEG 01.01.2022 Project funding
189785 Multichannel confocal microscope with fluorescence lifetime imaging for life science samples 01.03.2020 R'EQUIP
176222 Real-time detection of High-Frequency Oscillations with a Neuromorphic Device 01.01.2018 Project funding
146608 Spike-based computation and learning in distributed neuromorphic systems 01.08.2013 Project funding
136307 Formation and function of Drosophila taste circuits 01.01.2012 Sinergia
192617 Short-term memory in distinct reverberating thalamocortical loops 01.05.2020 Project funding


Brains learn from experience by adjusting their behavioral strategies to optimize a desired output, e.g., reward collection or danger avoidance. A key learning mechanism is to evaluate the mismatch between an internal model of the world and the actual interaction and to update the internal model according to this prediction error. Prediction errors can be used to correct bottom-up (recognition) and top-down (generative) signal flows by adjusting synaptic weights in the neural circuitry. In addition, a more global and delayed prediction error encoding a mismatch of desired and actual outcome value (‘reward prediction error’) can be propagated through the brain network and induce synaptic adaptations as well. Although prediction error handling and error propagation have been studied for decades, the principles of how neuronal networks in the brain update the internal model through synaptic plasticity remain poorly understood. Understanding the core mechanisms of error processing would not only provide fundamental insight into the amazing adaptive behaviors seen in the animal kingdom but also open new exciting avenues for innovative brain-inspired fast and self-learning neuromorphic computing systems. Here, we propose a new theory-driven approach validated by neuroscientific experiments to identify common error-coding principles in two animal models, the mouse and the fruit fly, combined with engineering approaches to build physical computing systems that embody the identified learning principles. The two animal models offer complementary physiological and genetic tool kits necessary to test theoretical hypotheses. Both will crucially profit from experimental setups that include real-time feedback modulation with neuromorphic electronic devices. Our central hypothesis is that generative and recognition errors are jointly processed in distinct dendritic compartments of individual neurons as well as in specific subtypes of inhibitory interneurons controlling these compartments. We will test this hypothesis by measuring neural activity during conditioned behaviors in the mouse barrel cortex and the fly mushroom body, and by specifically manipulating error-coding elements using optogenetic tools. With low-latency neuromorphic (‘spiking’) sensors and computing hardware we will establish a novel real-time closed-loop brain stimulation system enabling direct interactions with the error handling processes, either impeding or improving behavioral adaptations. Our interdisciplinary collaboration with complementary expertise will lay the ground for developing more evolved computational hypotheses regarding biological mechanisms underlying learning as well as novel neuromorphic hardware that guides the way to future self-learning computing devices.