Publications | Tetzlaff Research Group

2024

A Brain Machine Interface Framework for Exploring Proactive Control of Smart Environments

Jan-Matthias Braun, Michael Fauth, Michael Berger, and 11 more authors

2024

Abstract Bib Link

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.
@article{braun2024, title = {A Brain Machine Interface Framework for Exploring Proactive Control of Smart Environments}, author = {Braun, Jan-Matthias and Fauth, Michael and Berger, Michael and Huang, Nan-Sheng and Simeoni, Ezequiel and Gaeta, Eugenio and Rodrigues Do Carmo, Ricardo and García-Betances, Rebeca I. and Arredondo Waldmeyer, María Teresa and Gail, Alexander and Larsen, Jørgen C. and Manoonpong, Poramate and Tetzlaff, Christian and Wörgötter, Florentin}, year = {2024}, journaltitle = {Sci Rep}, volume = {14}, number = {1}, pages = {11054}, issn = {2045-2322}, doi = {10.1038/s41598-024-60280-7}, url = {https://www.nature.com/articles/s41598-024-60280-7}, }
Modeling Emergent Dynamics Arising from Synaptic Tagging and Capture at the Network Level

Jannik Luboeinski, and Christian Tetzlaff

In Synaptic Tagging and Capture: From Synapses to Behavior, 2024

Abstract Bib Link

The transfer from short-term memory, which persists only for a few hours, to long-term memory is assumed to begin with a process called synaptic consolidation. Drawing from the hypothesis that memories are mainly stored by the strength of synaptic connections, synaptic consolidation means that a memory representation is consolidated by the stabilization of previous synaptic changes. The major candidate mechanism assumed to underlie this consolidation at the cellular level is synaptic tagging and capture (STC). Previous studies have provided the first pieces of evidence for this assumption considering simple feed-forward networks. However, the recurrent connectivity of hippocampal or cortical neuronal networks plays a particularly important role in memory function, as it enables pattern completion and temporally prolonged sequences of activity. Such dynamics mainly arise from so-called cell assemblies, which are groups of neurons with particularly strong synaptic connections. Thus, while it has been proposed that STC implements synaptic consolidation, the link between the physiological mechanisms of STC and the cognitive functions of long-term memory remains unclear. On timescales of minutes to hours (i.e., on the timescales of STC), these functions include memory improvement, selective consolidation, retroactive interference, and priming of a particular memory.
@incollection{luboeinski2024, title = {Modeling Emergent Dynamics Arising from Synaptic Tagging and Capture at the Network Level}, booktitle = {Synaptic Tagging and Capture: From Synapses to Behavior}, author = {Luboeinski, Jannik and Tetzlaff, Christian}, editor = {Sajikumar, Sreedharan and Abel, Ted}, pages = {471--503}, publisher = {Springer International Publishing}, location = {Cham}, doi = {10.1007/978-3-031-54864-2_23}, url = {https://doi.org/10.1007/978-3-031-54864-2_23}, isbn = {978-3-031-54864-2}, year = {2024}, }
Unsupervised Learning of Perceptual Feature Combinations

Minija Tamosiunaite, Christian Tetzlaff, and Florentin Wörgötter

2024

Abstract Bib Link

In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and occurrence frequency. Here, we present a novel unsupervised learning mechanism that is largely independent of these contingencies and allows neurons in a network to achieve specificity for different feature combinations. This is achieved by a novel correlation-based (Hebbian) learning rule, which allows for linear weight growth and which is combined with a mechanism for gradually reducing the learning rate as soon as the neuron’s response becomes feature combination specific. In a set of control experiments, we show that other existing advanced learning rules cannot satisfactorily form ordered multi-feature representations. In addition, we show that networks, which use this type of learning always stabilize and converge to subsets of neurons with different feature-combination specificity. Neurons with this property may, thus, serve as an initial stage for the processing of ecologically relevant real world situations for an animal.
@article{tamosiunaite2024, title = {Unsupervised Learning of Perceptual Feature Combinations}, author = {Tamosiunaite, Minija and Tetzlaff, Christian and Wörgötter, Florentin}, editor = {Bush, Daniel}, year = {2024}, journaltitle = {PLoS Comput Biol}, volume = {20}, number = {3}, pages = {e1011926}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1011926}, url = {https://dx.plos.org/10.1371/journal.pcbi.1011926}, }

2023

Implementation of Input Correlation Learning with an Optoelectronic Dendritic Unit

Silvia Ortín, Miguel C. Soriano, Christian Tetzlaff, and 4 more authors

Frontiers in Physics, 2023

Abstract Bib Link

The implementation of machine learning concepts using optoelectronic and photonic components is rapidly advancing. Here, we use the recently introduced notion of optical dendritic structures, which aspires to transfer neurobiological principles to photonics computation. In real neurons, plasticity—the modification of the connectivity between neurons due to their activity—plays a fundamental role in learning. In the current work, we investigate theoretically and experimentally an artificial dendritic structure that implements a modified Hebbian learning model, called input correlation (ICO) learning. The presented optical fiber-based dendritic structure employs the summation of the different optical intensities propagating along the optical dendritic branches and uses Gigahertz-bandwidth modulation via semiconductor optical amplifiers to apply the necessary plasticity rules. In its full deployment, this optoelectronic ICO learning analog can be an efficient hardware platform for ultra-fast control.
@article{ortin2023, title = {Implementation of Input Correlation Learning with an Optoelectronic Dendritic Unit}, author = {Ort{\'i}n, Silvia and Soriano, Miguel C. and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin and Fischer, Ingo and Mirasso, Claudio R. and Argyris, Apostolos}, year = {2023}, journal = {Frontiers in Physics}, volume = {11}, issn = {2296-424X}, url = {https://www.frontiersin.org/articles/10.3389/fphy.2023.1112295}, }
High-Resolution Analysis of Bound Ca2+ in Neurons and Synapses

Elisa A. Bonnin, Arash Golmohammadi, Ronja Rehm, and 2 more authors

Life Science Alliance, Oct 2023

Abstract Bib Link

Calcium (Ca2+) is a well-known second messenger in all cells, and is especially relevant for neuronal activity. Neuronal Ca2+ is found in different forms, with a minority being freely soluble in the cell and more than 99% being bound to proteins. Free Ca2+ has received much attention over the last few decades, but protein-bound Ca2+ has been difficult to analyze. Here, we introduce correlative fluorescence and nanoscale secondary ion mass spectrometry imaging as a tool to describe bound Ca2+. As expected, bound Ca2+ is ubiquitous. It does not correlate to free Ca2+ dynamics at the whole-neuron level, but does correlate significantly to the intensity of markers for GABAergic pre-synapse and glutamatergic post-synapses. In contrast, a negative correlation to pre-synaptic activity was observed, with lower levels of bound Ca2+ observed in the more active synapses. We conclude that bound Ca2+ may regulate neuronal activity and should receive more attention in the future.
@article{bonnin2023, title = {High-Resolution Analysis of Bound Ca2+ in Neurons and Synapses}, author = {Bonnin, Elisa A. and Golmohammadi, Arash and Rehm, Ronja and Tetzlaff, Christian and Rizzoli, Silvio O.}, year = {2023}, month = oct, journal = {Life Science Alliance}, volume = {7}, number = {1}, publisher = {Life Science Alliance}, issn = {2575-1077}, doi = {10.26508/lsa.202302030}, url = {https://www.life-science-alliance.org/content/7/1/e202302030}, chapter = {Methods}, copyright = {{\textcopyright} 2023 Bonnin et al.. https://creativecommons.org/licenses/by/4.0/This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).}, pmid = {37833073}, }
Synaptic Diversity in ANNs Can Facilitate Faster Learning

Martin Hofmann, Moritz F. P. Becker, Christian Tetzlaff, and 1 more author

Feb 2023

Abstract Bib Link

Various advancements in artificial neural networks (ANNs) are inspired by biological concepts, e.g., the artificial neuron, an efficient model of biological nerve cells demonstrating learning capabilities on large amounts of data. More recent inspirations with promising results are advanced regularization techniques, e.g., synaptic scaling, and backpropagation alternatives, e.g., Targetprop. While neurosciences continuously discover and better understand the mechanisms of biological neural networks (BNNs), new opportunities for a transfer of these concepts towards ANNs arise. However, only few concepts are readily applicable and improvements for ANNs are far from being guaranteed. In this paper, we focus on the inhomogeneous and dynamically changing structures of BNNs in contrast to mostly homogeneous and fix topologies of ANNs. More specifically, we transfer concepts of synaptic diversity, namely spontaneous synaptic remodeling, diversity in synaptic plasticity and multi-synaptic connectivity to ANNs. We observe ANNs enhanced by synaptic diversity concepts to learn faster, to predict with higher accuracy and to be more resilient to gradient inversion attacks. Our proposed methods are easily applicable to existing ANN topologies and are therefore supposed to stimulate an adaptation of and further research into these mechanisms.
@article{hofmann2023, title = {Synaptic Diversity in ANNs Can Facilitate Faster Learning}, author = {Hofmann, Martin and Becker, Moritz F. P. and Tetzlaff, Christian and M{\"a}der, Patrick}, year = {2023}, month = feb, url = {https://openreview.net/forum?id=6vSDzn-4FlW}, }
Sparse Clustered Inhibition Projects Sequential Activity onto Unique Neural Subspaces

Andrew B. Lehr, Arvind Kumar, and Christian Tetzlaff

Sep 2023

Abstract Bib Link

Neural activity in the brain traces sequential trajectories on low dimensional subspaces. For flexible behavior, these neural subspaces must be manipulated and reoriented within tens of milliseconds. Using mathematical analysis and simulation of a recurrently connected neural circuit for sequence generation, we report that incorporating a subtype of interneurons that provides sparse but clustered inhibition enables the projection of sequential activity onto task- or context-specific neural subspaces. Depending on the sparsity of inhibitory projections, neural subspaces could be arbitrarily rotated with respect to the intrinsic subspace, without altering the key aspects of sequence generation. Thus, we propose a circuit motif and mechanism by which inhibitory interneurons can enable flexible switching between neural subspaces on a fast timescale of milliseconds, controlled by top down signals. Significance StatementSignificance Statement Cognitive faculties like memory, movement, and decision-making depend on the reliable sequential activation of neurons in the brain. These activity sequences reside on low dimensional neural subspaces, with different tasks or behaviors represented on different subspaces. Flexible cognition therefore requires changes in subspace orientation on timescales as fast as tens of milliseconds. Here we propose a mechanism for fast timescale control of the neural subspace in a recurrently connected neural network model for sequence generation. Subspace manipulation is implemented by inhibitory neurons that form sparse, clustered axonal projections onto the neurons responsible for sequence generation. Ensembles of these inhibitory neurons rapidly rotate the neural subspace and support storage of and dynamical switching between neural activity sequences on low dimensional manifolds.
@misc{lehr2023, title = {Sparse Clustered Inhibition Projects Sequential Activity onto Unique Neural Subspaces}, author = {Lehr, Andrew B. and Kumar, Arvind and Tetzlaff, Christian}, year = {2023}, month = sep, primaryclass = {New Results}, pages = {2023.09.15.557865}, publisher = {bioRxiv}, doi = {10.1101/2023.09.15.557865}, url = {https://www.biorxiv.org/content/10.1101/2023.09.15.557865v1}, archiveprefix = {bioRxiv}, chapter = {New Results}, copyright = {{\textcopyright} 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/}, }
Tunable Synaptic Working Memory with Volatile Memristive Devices

Saverio Ricci, David Kappel, Christian Tetzlaff, and 2 more authors

Neuromorphic Computing and Engineering, Dec 2023

Abstract Bib Link

Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In particular, the working memory (WM) utilizes mechanisms that span orders of magnitudes of timescales, from milliseconds to seconds or even minutes. This plentitude of timescales is an essential ingredient of WM tasks like visual or language processing. This degree of flexibility is challenging in analog computing hardware because it requires the integration of several reconfigurable capacitors of different size. Emerging volatile memristive devices present a compact and appealing solution to reproduce reconfigurable temporal dynamics in a neuromorphic network. We present a demonstration of WM using a silver-based memristive device whose key parameters, retention time and switching probability, can be electrically tuned and adapted to the task at hand. First, we demonstrate the principles of WM in a small scale hardware to execute an associative memory task. Then, we use the experimental data in two larger scale simulations, the first featuring WM in a biological environment, the second demonstrating associative symbolic WM.
@article{ricci2023, title = {Tunable Synaptic Working Memory with Volatile Memristive Devices}, author = {Ricci, Saverio and Kappel, David and Tetzlaff, Christian and Ielmini, Daniele and Covi, Erika}, year = {2023}, month = dec, journal = {Neuromorphic Computing and Engineering}, volume = {3}, number = {4}, pages = {044004}, issn = {2634-4386}, doi = {10.1088/2634-4386/ad01d6}, url = {https://iopscience.iop.org/article/10.1088/2634-4386/ad01d6}, }

2022

Emulating Dendritic Computing Paradigms on Analog Neuromorphic Hardware

Jakob Kaiser, Sebastian Billaudelle, Eric Müller, and 3 more authors

Neuroscience, May 2022

Abstract Bib Link

BrainScaleS-2 is an accelerated and highly configurable neuromorphic system with physical models of neurons and synapses. Beyond networks of spiking point neurons, it allows for the implementation of user-defined neuron morphologies. Both passive propagation of electric signals between compartments as well as dendritic spikes and plateau potentials can be emulated. In this paper, three multi-compartment neuron morphologies are chosen to demonstrate passive propagation of postsynaptic potentials, spatio-temporal coincidence detection of synaptic inputs in a dendritic branch, and the replication of the BAC burst firing mechanism found in layer 5 pyramidal neurons of the neocortex.
@article{kaiser2022, title = {Emulating Dendritic Computing Paradigms on Analog Neuromorphic Hardware}, author = {Kaiser, Jakob and Billaudelle, Sebastian and M{\"u}ller, Eric and Tetzlaff, Christian and Schemmel, Johannes and Schmitt, Sebastian}, year = {2022}, month = may, journal = {Neuroscience}, series = {Dendritic Contributions to Biological and Artificial Computations}, volume = {489}, pages = {290--300}, issn = {0306-4522}, doi = {10.1016/j.neuroscience.2021.08.013}, url = {https://www.sciencedirect.com/science/article/pii/S0306452221004218}, }
Synapses Learn to Utilize Pre-Synaptic Noise for the Prediction of Postsynaptic Dynamics

David Kappel, and Christian Tetzlaff

Apr 2022

Abstract Bib Link

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use their noise to represent uncertainties about the activity of the post-synaptic neuron. To show this we utilize the free-energy principle (FEP), a well-established theoretical framework to describe the ability of organisms to self-organize and survive in uncertain environments. This principle provides insights on multiple scales, from high-level behavioral functions such as attention or foraging, to the dynamics of single microcircuits in the brain, suggesting that the FEP can be used to describe all levels of brain function. The synapse-centric account of the FEP that is pursued here, suggests that synapses form an internal model of the somatic membrane dynamics, being updated by a synaptic learning rule that resembles experimentally well-established LTP/LTD mechanisms. This approach entails that a synapse utilizes noisy processes like stochastic synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate their noise processes to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.
@misc{kappel2022, title = {Synapses Learn to Utilize Pre-Synaptic Noise for the Prediction of Postsynaptic Dynamics}, author = {Kappel, David and Tetzlaff, Christian}, year = {2022}, month = apr, primaryclass = {New Results}, pages = {2022.04.22.489175}, publisher = {bioRxiv}, doi = {10.1101/2022.04.22.489175}, url = {https://www.biorxiv.org/content/10.1101/2022.04.22.489175v1}, archiveprefix = {bioRxiv}, chapter = {New Results}, copyright = {{\textcopyright} 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, }
Neuromodulator-Dependent Synaptic Tagging and Capture Retroactively Controls Neural Coding in Spiking Neural Networks

Andrew B. Lehr, Jannik Luboeinski, and Christian Tetzlaff

Scientific Reports, Oct 2022

Abstract Bib Link

Events that are important to an individual’s life trigger neuromodulator release in brain areas responsible for cognitive and behavioral function. While it is well known that the presence of neuromodulators such as dopamine and norepinephrine is required for memory consolidation, the impact of neuromodulator concentration is, however, less understood. In a recurrent spiking neural network model featuring neuromodulator-dependent synaptic tagging and capture, we study how synaptic memory consolidation depends on the amount of neuromodulator present in the minutes to hours after learning. We find that the storage of rate-based and spike timing-based information is controlled by the level of neuromodulation. Specifically, we find better recall of temporal information for high levels of neuromodulation, while we find better recall of rate-coded spatial patterns for lower neuromodulation, mediated by the selection of different groups of synapses for consolidation. Hence, our results indicate that in minutes to hours after learning, the level of neuromodulation may alter the process of synaptic consolidation to ultimately control which type of information becomes consolidated in the recurrent neural network.
@article{lehr2022, title = {Neuromodulator-Dependent Synaptic Tagging and Capture Retroactively Controls Neural Coding in Spiking Neural Networks}, author = {Lehr, Andrew B. and Luboeinski, Jannik and Tetzlaff, Christian}, year = {2022}, month = oct, journal = {Scientific Reports}, volume = {12}, number = {1}, pages = {17772}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-022-22430-7}, url = {https://www.nature.com/articles/s41598-022-22430-7}, copyright = {2022 The Author(s)}, }
Organization and Priming of Long-Term Memory Representations with Two-Phase Plasticity

Jannik Luboeinski, and Christian Tetzlaff

Cognitive Computation, Jun 2022

Abstract Bib Link

In recurrent neural networks in the brain, memories are represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons with particularly strong synaptic connections formed by synaptic plasticity and consolidated by synaptic tagging and capture (STC). To link these synaptic mechanisms to long-term memory on the level of cognition and behavior, their functional implications on the level of neural networks have to be understood.
@article{luboeinski2022, title = {Organization and Priming of Long-Term Memory Representations with Two-Phase Plasticity}, author = {Luboeinski, Jannik and Tetzlaff, Christian}, year = {2022}, month = jun, journal = {Cognitive Computation}, issn = {1866-9964}, doi = {10.1007/s12559-022-10021-7}, url = {https://doi.org/10.1007/s12559-022-10021-7}, }
Differential Hebbian Learning with Time-Continuous Signals for Active Noise Reduction

Konstantin Möller, David Kappel, Minija Tamosiunaite, and 3 more authors

PLOS ONE, May 2022

Abstract Bib Link

Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain.
@article{moller2022, title = {Differential Hebbian Learning with Time-Continuous Signals for Active Noise Reduction}, author = {M{\"o}ller, Konstantin and Kappel, David and Tamosiunaite, Minija and Tetzlaff, Christian and Porr, Bernd and W{\"o}rg{\"o}tter, Florentin}, year = {2022}, month = may, journal = {PLOS ONE}, volume = {17}, number = {5}, pages = {e0266679}, publisher = {Public Library of Science}, issn = {1932-6203}, doi = {10.1371/journal.pone.0266679}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266679}, }
Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories

Saverio Ricci, David Kappel, Christian Tetzlaff, and 2 more authors

Nov 2022

Abstract Bib Link

The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a spontaneous change of device conductance with a similarity to biological mechanisms. They rely on the formation and self-disruption of a metallic conductive filament through an oxide layer, with a retention time ranging from a few milliseconds to several seconds, greatly tunable according to the maximum current which is flowing through the device. Here we prove a neuromorphic system based on volatile-RRAMs able to mimic the principles of biological decision-making behavior and tackle the Two-Alternative Forced Choice problem, where a subject is asked to make a choice between two possible alternatives not relying on a precise knowledge of the problem, rather on noisy perceptions.
@misc{ricci2022, title = {Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories}, author = {Ricci, Saverio and Kappel, David and Tetzlaff, Christian and Ielmini, Daniele and Covi, Erika}, year = {2022}, month = nov, number = {arXiv:2211.03081}, eprint = {2211.03081}, primaryclass = {cs}, publisher = {arXiv}, doi = {10.48550/arXiv.2211.03081}, }

2021

The Biophysical Basis Underlying the Maintenance of Early Phase Long-Term Potentiation

Moritz FP Becker, and Christian Tetzlaff

PLoS computational biology, Nov 2021

Bib

@article{becker2021,
  title = {The Biophysical Basis Underlying the Maintenance of Early Phase Long-Term Potentiation},
  author = {Becker, Moritz FP and Tetzlaff, Christian},
  year = {2021},
  journal = {PLoS computational biology},
  volume = {17},
  number = {3},
  pages = {e1008813},
  publisher = {Public Library of Science San Francisco, CA USA},
}

Reproducing Asymmetrical Spine Shape Fluctuations in a Model of Actin Dynamics Predicts Self-Organized Criticality

Mayte Bonilla-Quintana, Florentin Wörgötter, Elisa D’Este, and 2 more authors

Scientific reports, Nov 2021

Bib

@article{bonilla-quintana2021,
  title = {Reproducing Asymmetrical Spine Shape Fluctuations in a Model of Actin Dynamics Predicts Self-Organized Criticality},
  author = {{Bonilla-Quintana}, Mayte and W{\"o}rg{\"o}tter, Florentin and D'Este, Elisa and Tetzlaff, Christian and Fauth, Michael},
  year = {2021},
  journal = {Scientific reports},
  volume = {11},
  number = {1},
  pages = {4012},
  publisher = {Nature Publishing Group UK London},
}

Editorial: Robust Artificial Intelligence for Neurorobotics

Joe Hays, Subramanian Ramamoorthy, and Christian Tetzlaff

Frontiers in Neurorobotics, Nov 2021

Bib Link

@article{hays2021,
  title = {Editorial: Robust Artificial Intelligence for Neurorobotics},
  author = {Hays, Joe and Ramamoorthy, Subramanian and Tetzlaff, Christian},
  year = {2021},
  journal = {Frontiers in Neurorobotics},
  volume = {15},
  issn = {1662-5218},
  url = {https://www.frontiersin.org/articles/10.3389/fnbot.2021.809903},
}

A Synapse-Centric Account of the Free Energy Principle

David Kappel, and Christian Tetzlaff

Mar 2021

Abstract Bib Link

The free energy principle (FEP) is a mathematical framework that describes how biological systems self-organize and survive in their environment. This principle provides insights on multiple scales, from high-level behavioral and cognitive functions such as attention or foraging, down to the dynamics of specialized cortical microcircuits, suggesting that the FEP manifests on several levels of brain function. Here, we apply the FEP to one of the smallest functional units of the brain: single excitatory synaptic connections. By focusing on an experimentally well understood biological system we are able to derive learning rules from first principles while keeping assumptions minimal. This synapse-centric account of the FEP predicts that synapses interact with the soma of the post-synaptic neuron through stochastic synaptic releases to probe their behavior and use back-propagating action potentials as feedback to update the synaptic weights. The emergent learning rules are regulated triplet STDP rules that depend only on the timing of the pre- and post-synaptic spikes and the internal states of the synapse. The parameters of the learning rules are fully determined by the parameters of the post-synaptic neuron model, suggesting a close interplay between the synaptic and somatic compartment and making precise predictions about the synaptic dynamics. The synapse-level uncertainties automatically lead to representations of uncertainty on the network level that manifest in ambiguous situations. We show that the FEP learning rules can be applied to spiking neural networks for supervised and unsupervised learning and for a closed loop learning task where a behaving agent interacts with an environment.
@misc{kappel2021, title = {A Synapse-Centric Account of the Free Energy Principle}, author = {Kappel, David and Tetzlaff, Christian}, year = {2021}, month = mar, number = {arXiv:2103.12649}, eprint = {2103.12649}, primaryclass = {q-bio}, publisher = {arXiv}, doi = {10.48550/arXiv.2103.12649}, }
Continual Learning with Memory Cascades

David Kappel, Francesco Negri, and Christian Tetzlaff

In I (Still) Can’t Believe It’s Not Better! NeurIPS 2021 Workshop, Dec 2021

Abstract Bib Link

Continual learning poses an important challenge to machine learning models. Kirkpatrick et al. introduced a model that combats forgetting during continual learning by using a Bayesian prior to transfer knowledge between task switches. This approach showed promising results but the algorithm was given access to the time points when tasks were switched. Using a model of stochastic learning dynamics we show that this model is very closely related to the previously developed cascade model to combat catastrophic forgetting. This general formulation allows us to use the model also for online learning where no knowledge about task switching times is given to the network. Also it allows us to use deeper hierarchies of Bayesian priors. We evaluate this model on the permuted MNIST task. We demonstrate improved task performance during task switching, but find that online learning is still significantly worse when task switching times are unknown to the network.
@inproceedings{kappel2021b, title = {Continual Learning with Memory Cascades}, booktitle = {I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop}, author = {Kappel, David and Negri, Francesco and Tetzlaff, Christian}, year = {2021}, month = dec, url = {https://openreview.net/forum?id=E1xIZf0E7qr}, }

CA2 beyond Social Memory: Evidence for a Fundamental Role in Hippocampal Information Processing

Andrew B. Lehr, Arvind Kumar, Christian Tetzlaff, and 3 more authors

Neuroscience & Biobehavioral Reviews, Dec 2021

Bib

@article{lehr2021a,
  title = {CA2 beyond Social Memory: Evidence for a Fundamental Role in Hippocampal Information Processing},
  author = {Lehr, Andrew B. and Kumar, Arvind and Tetzlaff, Christian and Hafting, Torkel and Fyhn, Marianne and St{\"o}ber, Tristan M.},
  year = {2021},
  journal = {Neuroscience \& Biobehavioral Reviews},
  volume = {126},
  pages = {398--412},
  publisher = {Elsevier},
}

A Local Circadian Clock for Memory?

Andrew B. Lehr, Robert J. McDonald, Christina M. Thorpe, and 2 more authors

Neuroscience & Biobehavioral Reviews, Dec 2021

Bib

@article{lehr2021b,
  title = {A Local Circadian Clock for Memory?},
  author = {Lehr, Andrew B. and McDonald, Robert J. and Thorpe, Christina M. and Tetzlaff, Christian and Deibel, Scott H.},
  year = {2021},
  journal = {Neuroscience \& Biobehavioral Reviews},
  volume = {127},
  pages = {946--957},
  publisher = {Elsevier},
}

Memory Consolidation and Improvement by Synaptic Tagging and Capture in Recurrent Neural Networks

Jannik Luboeinski, and Christian Tetzlaff

Communications Biology, Mar 2021

Abstract Bib Link

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 consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.
@article{luboeinski2021, title = {Memory Consolidation and Improvement by Synaptic Tagging and Capture in Recurrent Neural Networks}, author = {Luboeinski, Jannik and Tetzlaff, Christian}, year = {2021}, month = mar, journal = {Communications Biology}, volume = {4}, number = {1}, pages = {1--17}, publisher = {Nature Publishing Group}, issn = {2399-3642}, doi = {10.1038/s42003-021-01778-y}, url = {https://www.nature.com/articles/s42003-021-01778-y}, copyright = {2021 The Author(s)}, }

Brian2Loihi: An Emulator for the Neuromorphic Chip Loihi Using the Spiking Neural Network Simulator Brian

Carlo Michaelis, Andrew B. Lehr, Winfried Oed, and 1 more author

arXiv preprint arXiv:2109.12308, Mar 2021

Bib

@article{michaelis2021,
  title = {Brian2Loihi: An Emulator for the Neuromorphic Chip Loihi Using the Spiking Neural Network Simulator Brian},
  author = {Michaelis, Carlo and Lehr, Andrew B. and Oed, Winfried and Tetzlaff, Christian},
  year = {2021},
  journal = {arXiv preprint arXiv:2109.12308},
  eprint = {2109.12308},
}

Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

Daniel Miner, Florentin Wörgötter, Christian Tetzlaff, and 1 more author

Biology, Mar 2021

Bib

@article{miner2021,
  title = {Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission},
  author = {Miner, Daniel and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian and Fauth, Michael},
  year = {2021},
  journal = {Biology},
  volume = {10},
  number = {7},
  pages = {577},
  publisher = {MDPI},
}

2020

The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations

Johannes Maria Auth, Timo Nachstedt, and Christian Tetzlaff

Frontiers in Neural Circuits, Mar 2020

Abstract Bib Link

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 through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.
@article{auth2020, title = {The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations}, author = {Auth, Johannes Maria and Nachstedt, Timo and Tetzlaff, Christian}, year = {2020}, journal = {Frontiers in Neural Circuits}, volume = {14}, issn = {1662-5110}, url = {https://www.frontiersin.org/articles/10.3389/fncir.2020.541728}, }

Modeling the Shape of Synaptic Spines by Their Actin Dynamics

Mayte Bonilla-Quintana, Florentin Wörgötter, Christian Tetzlaff, and 1 more author

Frontiers in synaptic neuroscience, Mar 2020

Bib

@article{bonilla-quintana2020,
  title = {Modeling the Shape of Synaptic Spines by Their Actin Dynamics},
  author = {{Bonilla-Quintana}, Mayte and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian and Fauth, Michael},
  year = {2020},
  journal = {Frontiers in synaptic neuroscience},
  volume = {12},
  pages = {9},
  publisher = {Frontiers Media SA},
}

Evolving Artificial Neural Networks with Feedback

Sebastian Herzog, Christian Tetzlaff, and Florentin Wörgötter

Neural Networks, Mar 2020

Bib

@article{herzog2020,
  title = {Evolving Artificial Neural Networks with Feedback},
  author = {Herzog, Sebastian and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin},
  year = {2020},
  journal = {Neural Networks},
  volume = {123},
  pages = {153--162},
  publisher = {Elsevier},
}

The Self-Organized Learning of Noisy Environmental Stimuli Requires Distinct Phases of Plasticity

Steffen Krüppel, and Christian Tetzlaff

Network Neuroscience, Mar 2020

Bib

@article{kruppel2020,
  title = {The Self-Organized Learning of Noisy Environmental Stimuli Requires Distinct Phases of Plasticity},
  author = {Kr{\"u}ppel, Steffen and Tetzlaff, Christian},
  year = {2020},
  journal = {Network Neuroscience},
  volume = {4},
  number = {1},
  pages = {174--199},
  publisher = {MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info {\ldots}},
}

Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi

Carlo Michaelis, Andrew B. Lehr, and Christian Tetzlaff

Frontiers in Neurorobotics, Mar 2020

Abstract Bib Link

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 are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel’s neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.
@article{michaelis2020, title = {Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi}, author = {Michaelis, Carlo and Lehr, Andrew B. and Tetzlaff, Christian}, year = {2020}, journal = {Frontiers in Neurorobotics}, volume = {14}, issn = {1662-5218}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2020.589532}, }

Hey, Look over There: Distraction Effects on Rapid Sequence Recall

Daniel Miner, and Christian Tetzlaff

Plos one, Mar 2020

Bib

@article{miner2020,
  title = {Hey, Look over There: Distraction Effects on Rapid Sequence Recall},
  author = {Miner, Daniel and Tetzlaff, Christian},
  year = {2020},
  journal = {Plos one},
  volume = {15},
  number = {4},
  pages = {e0223743},
  publisher = {Public Library of Science San Francisco, CA USA},
}

Quantitative Synaptic Biology: A Perspective on Techniques, Numbers and Expectations

Sofiia Reshetniak, Rubén Fernández-Busnadiego, Marcus Müller, and 2 more authors

International Journal of Molecular Sciences, Oct 2020

Abstract Bib Link

Synapses play a central role for the processing of information in the brain and have been analyzed in countless biochemical, electrophysiological, imaging, and computational studies. The functionality and plasticity of synapses are nevertheless still difficult to predict, and conflicting hypotheses have been proposed for many synaptic processes. In this review, we argue that the cause of these problems is a lack of understanding of the spatiotemporal dynamics of key synaptic components. Fortunately, a number of emerging imaging approaches, going beyond super-resolution, should be able to provide required protein positions in space at different points in time. Mathematical models can then integrate the resulting information to allow the prediction of the spatiotemporal dynamics. We argue that these models, to deal with the complexity of synaptic processes, need to be designed in a sufficiently abstract way. Taken together, we suggest that a well-designed combination of imaging and modelling approaches will result in a far more complete understanding of synaptic function than currently possible.
@article{reshetniak2020, title = {Quantitative Synaptic Biology: A Perspective on Techniques, Numbers and Expectations}, author = {Reshetniak, Sofiia and {Fern{\'a}ndez-Busnadiego}, Rub{\'e}n and M{\"u}ller, Marcus and Rizzoli, Silvio O. and Tetzlaff, Christian}, year = {2020}, month = oct, journal = {International Journal of Molecular Sciences}, volume = {21}, number = {19}, pages = {7298}, issn = {1422-0067}, doi = {10.3390/ijms21197298}, url = {https://www.mdpi.com/1422-0067/21/19/7298}, }

2019

Principles Underlying the Input-Dependent Formation and Organization of Memories

Juliane Herpich, and Christian Tetzlaff

Network Neuroscience, Oct 2019

Bib

@article{herpich2019,
  title = {Principles Underlying the Input-Dependent Formation and Organization of Memories},
  author = {Herpich, Juliane and Tetzlaff, Christian},
  year = {2019},
  journal = {Network Neuroscience},
  volume = {3},
  number = {2},
  pages = {606--634},
  publisher = {MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info {\ldots}},
}

A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity

Janne Lappalainen, Juliane Herpich, and Christian Tetzlaff

Frontiers in Computational Neuroscience, Oct 2019

Abstract Bib Link

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, these models are mathematically complex impeding the comprehensive investigation of their link to cognitive processes generally executed on the neuronal network level. Here, we derive a mathematical framework enabling the simplification of such detailed models of synaptic plasticity facilitating further mathematical analyses. By this framework we obtain a compact, firing-rate-dependent mathematical formulation, which includes the essential dynamics of the detailed model and, thus, of experimentally verified properties of synaptic plasticity. Amongst others, by testing our framework by abstracting the dynamics of two well-established calcium-dependent synaptic plasticity models, we derived that the synaptic changes depend on the square of the presynaptic firing rate, which is in contrast to previous assumptions. Thus, the here-presented framework enables the derivation of biologically plausible but simple mathematical models of synaptic plasticity allowing to analyze the underlying dependencies of synaptic dynamics from neuronal properties such as the firing rate and to investigate their implications in complex neuronal networks.
@article{lappalainen2019, title = {A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity}, author = {Lappalainen, Janne and Herpich, Juliane and Tetzlaff, Christian}, year = {2019}, journal = {Frontiers in Computational Neuroscience}, volume = {13}, issn = {1662-5188}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2019.00026}, }

2018

Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology

Poramate Manoonpong, and Christian Tetzlaff

Oct 2018

Bib

@misc{manoonpong2018,
  title = {Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology},
  author = {Manoonpong, Poramate and Tetzlaff, Christian},
  year = {2018},
  journal = {Frontiers in Neurorobotics},
  volume = {12},
  pages = {53},
  publisher = {Frontiers Media SA},
}

2017

Long-Term Information Storage by the Interaction of Synaptic and Structural Plasticity

Michael Fauth, Florentin Wörgötter, and Christian Tetzlaff

In The Rewiring Brain, Oct 2017

Bib

@incollection{fauth2017,
  title = {Long-Term Information Storage by the Interaction of Synaptic and Structural Plasticity},
  booktitle = {The Rewiring Brain},
  author = {Fauth, Michael and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2017},
  pages = {343--360},
  publisher = {Elsevier},
}

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control

Timo Nachstedt, Christian Tetzlaff, and Poramate Manoonpong

Frontiers in neurorobotics, Oct 2017

Bib

@article{nachstedt2017,
  title = {Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control},
  author = {Nachstedt, Timo and Tetzlaff, Christian and Manoonpong, Poramate},
  year = {2017},
  journal = {Frontiers in neurorobotics},
  volume = {11},
  pages = {14},
  publisher = {Frontiers Media SA},
}

Working Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs

Timo Nachstedt, and Christian Tetzlaff

Scientific reports, Oct 2017

Bib

@article{nachstedt2017a,
  title = {Working Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs},
  author = {Nachstedt, Timo and Tetzlaff, Christian},
  year = {2017},
  journal = {Scientific reports},
  volume = {7},
  number = {1},
  pages = {2473},
  publisher = {Nature Publishing Group UK London},
}

2016

Opposing Effects of Neuronal Activity on Structural Plasticity

Michael Fauth, and Christian Tetzlaff

Frontiers in Neuroanatomy, Oct 2016

Abstract Bib Link

The connectivity of the brain is continuously adjusted to new environmental influences by several activity-dependent adaptive processes. The most investigated adaptive mechanism is activity-dependent functional or synaptic plasticity regulating the transmission efficacy of existing synapses. Another important but less prominently discussed adaptive process is structural plasticity, which changes the connectivity by the formation and deletion of synapses. In this review, we show, based on experimental evidence, that structural plasticity can be classified similar to synaptic plasticity into two categories: (i) Hebbian structural plasticity, which leads to an increase (decrease) of the number of synapses during phases of high (low) neuronal activity and (ii) homeostatic structural plasticity, which balances these changes by removing and adding synapses. Furthermore, based on experimental and theoretical insights, we argue that each type of structural plasticity fulfills a different function. While Hebbian structural changes enhance memory lifetime, storage capacity, and memory robustness, homeostatic structural plasticity self-organizes the connectivity of the neural network to assure stability. However, the link between functional synaptic and structural plasticity as well as the detailed interactions between Hebbian and homeostatic structural plasticity are more complex. This implies even richer dynamics requiring further experimental and theoretical investigations.
@article{fauth2016, title = {Opposing Effects of Neuronal Activity on Structural Plasticity}, author = {Fauth, Michael and Tetzlaff, Christian}, year = {2016}, journal = {Frontiers in Neuroanatomy}, volume = {10}, issn = {1662-5129}, url = {https://www.frontiersin.org/articles/10.3389/fnana.2016.00075}, }

2015

Stimulus Discrimination and Association with Hebbian Cell Assemblies

Johannes M. Auth, Timo Nachstedt, Christian Tetzlaff, and 1 more author

BMC Neuroscience, Dec 2015

Bib Link

@article{auth2015,
  title = {Stimulus Discrimination and Association with Hebbian Cell Assemblies},
  author = {Auth, Johannes M. and Nachstedt, Timo and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin},
  year = {2015},
  month = dec,
  journal = {BMC Neuroscience},
  volume = {16},
  number = {1},
  pages = {P287},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-16-S1-P287},
  url = {https://doi.org/10.1186/1471-2202-16-S1-P287},
}

Self-Organization of Computation in Neural Systems by Interaction between Homeostatic and Synaptic Plasticity

Sakyasingha Dasgupta, Christian Tetzlaff, Tomas Kulvicius, and 1 more author

BMC Neuroscience, Dec 2015

Bib Link

@article{dasgupta2015,
  title = {Self-Organization of Computation in Neural Systems by Interaction between Homeostatic and Synaptic Plasticity},
  author = {Dasgupta, Sakyasingha and Tetzlaff, Christian and Kulvicius, Tomas and W{\"o}rg{\"o}tter, Florentin},
  year = {2015},
  month = dec,
  journal = {BMC Neuroscience},
  volume = {16},
  number = {1},
  pages = {O5},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-16-S1-O5},
  url = {https://doi.org/10.1186/1471-2202-16-S1-O5},
}

The Formation of Multi-Synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences

Michael Fauth, Florentin Wörgötter, and Christian Tetzlaff

PLOS Computational Biology, Jan 2015

Abstract Bib Link

Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3–8) of synapses. In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have. We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems. Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities. Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes. These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.
@article{fauth2015, title = {The Formation of Multi-Synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences}, author = {Fauth, Michael and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian}, year = {2015}, month = jan, journal = {PLOS Computational Biology}, volume = {11}, number = {1}, pages = {e1004031}, publisher = {Public Library of Science}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1004031}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004031}, }

Collective Information Storage in Multiple Synapses Enables Fast Learning and Slow Forgetting

Michael J. Fauth, Florentin Wörgötter, and Christian Tetzlaff

BMC Neuroscience, Jan 2015

Bib

@article{fauth2015a,
  title = {Collective Information Storage in Multiple Synapses Enables Fast Learning and Slow Forgetting},
  author = {Fauth, Michael J. and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2015},
  journal = {BMC Neuroscience},
  volume = {16},
  number = {Suppl 1},
  pages = {O15},
  publisher = {Springer},
}

Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover

Michael Fauth, Florentin Wörgötter, and Christian Tetzlaff

PLoS computational biology, Jan 2015

Bib

@article{fauth2015b,
  title = {Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover},
  author = {Fauth, Michael and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2015},
  journal = {PLoS computational biology},
  volume = {11},
  number = {12},
  pages = {e1004684},
  publisher = {Public Library of Science San Francisco, CA USA},
}

Synaptic Plasticity in a Recurrent Neural Network for Versatile and Adaptive Behaviors of a Walking Robot

Eduard Grinke, Christian Tetzlaff, Florentin Wörgötter, and 1 more author

Frontiers in Neurorobotics, Jan 2015

Abstract Bib Link

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.
@article{grinke2015, title = {Synaptic Plasticity in a Recurrent Neural Network for Versatile and Adaptive Behaviors of a Walking Robot}, author = {Grinke, Eduard and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin and Manoonpong, Poramate}, year = {2015}, journal = {Frontiers in Neurorobotics}, volume = {9}, issn = {1662-5218}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2015.00011}, }

Interaction between Memories in an Abstract Mathematical Model Based on the Hebbian Cell Assembly Hypothesis

Juliane Herpich, Florentin Wörgötter, and Christian Tetzlaff

BMC Neuroscience, Dec 2015

Bib Link

@article{herpich2015,
  title = {Interaction between Memories in an Abstract Mathematical Model Based on the Hebbian Cell Assembly Hypothesis},
  author = {Herpich, Juliane and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2015},
  month = dec,
  journal = {BMC Neuroscience},
  volume = {16},
  number = {1},
  pages = {P253},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-16-S1-P253},
  url = {https://doi.org/10.1186/1471-2202-16-S1-P253},
}

Towards a Biological Plausible Model of the Interaction of Long-Term Memory and Working Memory

Timo Nachstedt, Florentin Wörgötter, and Christian Tetzlaff

BMC Neuroscience, Dec 2015

Bib

@article{nachstedt2015,
  title = {Towards a Biological Plausible Model of the Interaction of Long-Term Memory and Working Memory},
  author = {Nachstedt, Timo and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2015},
  journal = {BMC Neuroscience},
  volume = {16},
  number = {1},
  pages = {1--2},
  publisher = {BioMed Central},
}

The Use of Hebbian Cell Assemblies for Nonlinear Computation

Christian Tetzlaff, Sakyasingha Dasgupta, Tomas Kulvicius, and 1 more author

Scientific Reports, Aug 2015

Abstract Bib Link

When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a network with multiple, simultaneously active and computationally powerful cell assemblies is created. How such ordered structures are formed while preserving a rich diversity of neural dynamics needed for computation is still unknown. Here we show that the combination of synaptic plasticity with the slower process of synaptic scaling achieves (i) the formation of cell assemblies and (ii) enhances the diversity of neural dynamics facilitating the learning of complex calculations. Due to synaptic scaling the dynamics of different cell assemblies do not interfere with each other. As a consequence, this type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and – for execution – must cooperate with each other without interference. This mechanism, thus, permits the self-organization of computationally powerful sub-structures in dynamic networks for behavior control.
@article{tetzlaff2015, title = {The Use of Hebbian Cell Assemblies for Nonlinear Computation}, author = {Tetzlaff, Christian and Dasgupta, Sakyasingha and Kulvicius, Tomas and W{\"o}rg{\"o}tter, Florentin}, year = {2015}, month = aug, journal = {Scientific Reports}, volume = {5}, number = {1}, pages = {12866}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/srep12866}, url = {https://www.nature.com/articles/srep12866}, copyright = {2015 The Author(s)}, }

2014

Self-Organized Cell Assembly Formation

Timo Nachstedt, Florentin Wörgötter, and Christian Tetzlaff

BMC Neuroscience, Jul 2014

Bib Link

@article{nachstedt2014,
  title = {Self-Organized Cell Assembly Formation},
  author = {Nachstedt, Timo and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian},
  year = {2014},
  month = jul,
  journal = {BMC Neuroscience},
  volume = {15},
  number = {1},
  pages = {P30},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-15-S1-P30},
  url = {https://doi.org/10.1186/1471-2202-15-S1-P30},
}

The Association between Cell Assemblies and Transient Dynamics

Christian Tetzlaff, Sakyasingha Dasgupta, and Florentin Wörgötter

BMC Neuroscience, Jul 2014

Bib Link

@article{tetzlaff2014,
  title = {The Association between Cell Assemblies and Transient Dynamics},
  author = {Tetzlaff, Christian and Dasgupta, Sakyasingha and W{\"o}rg{\"o}tter, Florentin},
  year = {2014},
  month = jul,
  journal = {BMC Neuroscience},
  volume = {15},
  number = {1},
  pages = {P10},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-15-S1-P10},
  url = {https://doi.org/10.1186/1471-2202-15-S1-P10},
}

2013

An Information Theoretic Model of Information Processing in the Drosophila Olfactory System: The Role of Inhibitory Neurons for System Efficiency

Faramarz Faghihi, Christoph Kolodziejski, André Fiala, and 2 more authors

Frontiers in Computational Neuroscience, Jul 2013

Abstract Bib Link

Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the fly’s olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons (Kenyon cells) was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the system efficiency will be substantially reduced.
@article{faghihi2013, title = {An Information Theoretic Model of Information Processing in the Drosophila Olfactory System: The Role of Inhibitory Neurons for System Efficiency}, author = {Faghihi, Faramarz and Kolodziejski, Christoph and Fiala, Andr{\'e} and W{\"o}rg{\"o}tter, Florentin and Tetzlaff, Christian}, year = {2013}, journal = {Frontiers in Computational Neuroscience}, volume = {7}, issn = {1662-5188}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2013.00183}, }

Modelling the Interaction of Structural and Synaptic Plasticity

Michael Fauth, Christian Tetzlaff, and Florentin Wörgötter

BMC Neuroscience, Jul 2013

Bib Link

@article{fauth2013,
  title = {Modelling the Interaction of Structural and Synaptic Plasticity},
  author = {Fauth, Michael and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin},
  year = {2013},
  month = jul,
  journal = {BMC Neuroscience},
  volume = {14},
  number = {1},
  pages = {P416},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-14-S1-P416},
  url = {https://doi.org/10.1186/1471-2202-14-S1-P416},
}

Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation

Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, and 2 more authors

PLOS Computational Biology, Oct 2013

Abstract Bib Link

Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling – a slow process usually associated with the maintenance of activity homeostasis – combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short- from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes.
@article{tetzlaff2013, title = {Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation}, author = {Tetzlaff, Christian and Kolodziejski, Christoph and Timme, Marc and Tsodyks, Misha and W{\"o}rg{\"o}tter, Florentin}, year = {2013}, month = oct, journal = {PLOS Computational Biology}, volume = {9}, number = {10}, pages = {e1003307}, publisher = {Public Library of Science}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003307}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003307}, }

The Interaction of Synaptic Plasticity and Scaling and Their Role in Memory Formation

Christian Tetzlaff

Georg-August-Universität Göttingen, Oct 2013

Bib

@phdthesis{tetzlaff2013a,
  title = {The Interaction of Synaptic Plasticity and Scaling and Their Role in Memory Formation},
  author = {Tetzlaff, Christian},
  year = {2013},
  school = {Georg-August-Universit{\"a}t G{\"o}ttingen},
}

2012

Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks

Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, and 1 more author

Frontiers in Computational Neuroscience, Oct 2012

Abstract Bib Link

Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses toward stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties.
@article{tetzlaff2012, title = {Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks}, author = {Tetzlaff, Christian and Kolodziejski, Christoph and Timme, Marc and W{\"o}rg{\"o}tter, Florentin}, year = {2012}, journal = {Frontiers in Computational Neuroscience}, volume = {6}, issn = {1662-5188}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2012.00036}, }
Time Scales of Memory, Learning, and Plasticity

Christian Tetzlaff, Christoph Kolodziejski, Irene Markelic, and 1 more author

Biological Cybernetics, Dec 2012

Abstract Bib Link

After only about 10 days would the storage capacity of our nervous system be reached if we stored every bit of input. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.
@article{tetzlaff2012b, title = {Time Scales of Memory, Learning, and Plasticity}, author = {Tetzlaff, Christian and Kolodziejski, Christoph and Markelic, Irene and W{\"o}rg{\"o}tter, Florentin}, year = {2012}, month = dec, journal = {Biological Cybernetics}, volume = {106}, number = {11}, pages = {715--726}, issn = {1432-0770}, doi = {10.1007/s00422-012-0529-z}, url = {https://doi.org/10.1007/s00422-012-0529-z}, }

2011

Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity

Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, and 1 more author

Frontiers in Computational Neuroscience, Dec 2011

Abstract Bib Link

Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.
@article{tetzlaff2011, title = {Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity}, author = {Tetzlaff, Christian and Kolodziejski, Christoph and Timme, Marc and W{\"o}rg{\"o}tter, Florentin}, year = {2011}, journal = {Frontiers in Computational Neuroscience}, volume = {5}, issn = {1662-5188}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2011.00047}, }

Synaptic Scaling Generically Stabilizes Circuit Connectivity

Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, and 1 more author

BMC Neuroscience, Jul 2011

Bib Link

@article{tetzlaff2011a,
  title = {Synaptic Scaling Generically Stabilizes Circuit Connectivity},
  author = {Tetzlaff, Christian and Kolodziejski, Christoph and Timme, Marc and W{\"o}rg{\"o}tter, Florentin},
  year = {2011},
  month = jul,
  journal = {BMC Neuroscience},
  volume = {12},
  number = {1},
  pages = {P372},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-12-S1-P372},
  url = {https://doi.org/10.1186/1471-2202-12-S1-P372},
}

2010

Closed-Form Treatment of the Interactions between Neuronal Activity and Timing-Dependent Plasticity in Networks of Linear Neurons

Christoph Kolodziejski, Christian Tetzlaff, and Florentin Wörgötter

Frontiers in Computational Neuroscience, Jul 2010

Abstract Bib Link

Network activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structure of the network. To address this question a time-continuous treatment of plasticity is required. However, this is - even in simple recurrent network structures - currently not possible. Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. We use noisy periodic external input signals, which through the recurrent connections lead to complex actual ongoing inputs and observe that large stable ranges emerge in these networks without boundaries or weight-normalization. Somewhat counter-intuitively, we find that about 40% of these cases are obtained with a long-term potentiation-dominated STDP curve. Noise can reduce stability in some cases, but generally this does not occur. Instead stable domains are often enlarged. This study is a first step toward a better understanding of the ongoing interactions between activity and plasticity in recurrent networks using STDP. The results suggest that stability of (sub-)networks should generically be present also in larger structures.
@article{kolodziejski2010, title = {Closed-Form Treatment of the Interactions between Neuronal Activity and Timing-Dependent Plasticity in Networks of Linear Neurons}, author = {Kolodziejski, Christoph and Tetzlaff, Christian and W{\"o}rg{\"o}tter, Florentin}, year = {2010}, journal = {Frontiers in Computational Neuroscience}, volume = {4}, issn = {1662-5188}, url = {https://www.frontiersin.org/articles/10.3389/fncom.2010.00134}, }
Self-Organized Criticality in Developing Neuronal Networks

Christian Tetzlaff, Samora Okujeni, Ulrich Egert, and 2 more authors

PLOS Computational Biology, Dec 2010

Abstract Bib Link

Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.
@article{tetzlaff2010, title = {Self-Organized Criticality in Developing Neuronal Networks}, author = {Tetzlaff, Christian and Okujeni, Samora and Egert, Ulrich and W{\"o}rg{\"o}tter, Florentin and Butz, Markus}, year = {2010}, month = dec, journal = {PLOS Computational Biology}, volume = {6}, number = {12}, pages = {e1001013}, publisher = {Public Library of Science}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1001013}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001013}, }

2009

Self-Organized Criticality of Developing Artificial Neuronal Networks and Dissociated Cell Cultures

Christian Tetzlaff, Samora Okujeni, Ulrich Egert, and 2 more authors

BMC Neuroscience, Jul 2009

Bib Link

@article{tetzlaff2009,
  title = {Self-Organized Criticality of Developing Artificial Neuronal Networks and Dissociated Cell Cultures},
  author = {Tetzlaff, Christian and Okujeni, Samora and Egert, Ulrich and W{\"o}rg{\"o}tter, Florentin and Butz, Markus},
  year = {2009},
  month = jul,
  journal = {BMC Neuroscience},
  volume = {10},
  number = {1},
  pages = {P215},
  issn = {1471-2202},
  doi = {10.1186/1471-2202-10-S1-P215},
  url = {https://doi.org/10.1186/1471-2202-10-S1-P215},
}