Computational Neuroscience

cheng lab members 2022

We investigate the cognitive and neural mechanisms underlying learning and memory using computational methods. We welcome  students who would like to pursue a Bachelor/Master thesis or internship in our group. The main language of communication is English.

Our Research:

A particular focus of our research is understanding the function of a brain region, called the hippocampus. It is involved in storing and retrieving episodic memories and in generating representations of space. However, it remains unclear why these two functions go together and how these functions are implemented in the hippocampus. To address these two questions, we employ a number of computational and theoretical approaches, including

  • biologically realistic neural network models, which nonetheless are highly simplified, that capture the essence of the neural circuit mechanism underlying learning and memory.
  • algorithmic models of the storage and retrieval of episodic memories.
  • conceptual models of the nature of episodic memory.
  • robotics simulations of spatial memory in rodents.

The two projects described below exemplify the approach of our group.

Modeling spatial learning and navigation

Rodents are known to perform extremely well when navigating complex environments and to adapt rapidly to novel situations. They maneuver through dark sewers while looking for food or fleeing from predators and seem to know exactly where they are and where to go. We model the spatial behavior of rodents using a robot model. A particularly interesting question is: how do rodents learn to navigate novel environments and to perform new tasks? We study this question using a machine learning approach called "reinforcement learning". Within this framework, we implement the neural circuitry of the hippocampus to study the neural mechanisms underlying spatial learning and navigation. For more details see the "Projects" tab above.

Neural sequences in the hippocampus

The Nobel Prize in Physiology or Medicine in 2014 was awarded to John O’Keefe for the discovery of place cells and to May-Britt Moser and Edvard Moser for the discovery of grid cells. Place cells are found in the hippocampus and they respond, or “spike”, when an animal is located in a particular place, called a “place field”. The timing of spiking is such that, during one cycle of the theta oscillation (8 – 12Hz), groups of place cells spike in a sequence that corresponds to the order of their place fields in space (theta sequences). Intriguingly, the same place cells also spike in sequential order afterwards, when the animal is resting or sleeping (replay sequences). We are developing neural network models to help us understand how theta and replay sequences are generated and what role they play in learning and memory. 

Affiliations:


Speaker & projects P0, P2, and P5

Projects A14 & F01 Executive board


International collaborations:

  • Paulo de Almeida Ribeiro, Federal University of Maranhão, Brazil
  • Yuri Dabaghian, The University of Texas Health Science Center at Houston, USA
  • Kamran Diba, University of Michigan, USA
  • Lynn Nadel, University of Arizona, USA
  • Henry Otgaar, Maastricht University, Netherlands
  • Edison Pignaton de Freitas, Federal University of Rio Grande do Sul, Brazil
  • Bailu Si, School of Systems Science, Beijing Normal University, China
  • Thomas Suddendorf, University of Queensland, Australia
  • Alireza Valizadeh, Institute of Advanced Studies, Zanjan, Iran

CoBeL-RL

CoBeL-RL, a closed-loop simulator of complex behavior and learning based on Reinforcement Learning (RL) and deep neural networks, provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g., T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g., a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g., Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. For more information please read CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Link: https://github.com/sencheng/CoBeL-RL

License: GNU General Public License v3.0

Publications:

Diekmann, N., Vijayabaskaran, S., Zeng, X., Kappel, D., Menezes, M. C., & Cheng, S. (2023). CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning. Frontiers in Neuroinformatics, 17, 1134405. https://doi.org/10.3389/fninf.2023.1134405

Diekmann, N., & Cheng, S. (2023). A Model of Hippocampal Replay Driven by Experience and Environmental Structure Facilitates Spatial Learning. eLife, 12:e82301. https://doi.org/10.7554/eLife.82301

Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S. (2023). Modeling the function of episodic memory in spatial learning. Frontiers in Psychology, 14, 1160648. https://doi.org/10.3389/fpsyg.2023.1160648

Vijayabaskaran, S., & Cheng, S. (2022). Navigation task and action space drive the emergence of egocentric and allocentric spatial representations. PLOS Computational Biology, 18(10), e1010320. https://doi.org/10.1371/journal.pcbi.1010320

Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S. (2021). Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach. Scientific Reports, 11(1), Article 1. https://doi.org/10.1038/s41598-021-81157-z

Diekmann, N., Walther, T., Vijayabaskaran, S., & Cheng, S. (2019, September 15). Deep reinforcement learning in a spatial navigation task: Multiple contexts and their representation [Poster presentation]. Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://ccneuro.org/2019/Papers/ViewPapers.asp?PaperNum=1151

Data Analysis

⁃ Pyka-Parametric-Anatomical-Modeling-2014

Parametric Anatomical Modeling is a method to translate large-scale anatomical data into spiking neural networks. PAM is implemented as a Blender addon.

LICENSE: GNU GPL v2.0
DOI: 10.5281/zenodo.3298590
PUBLICATIONS: Pyka, M., Klatt, S., & Cheng, S. (2014). Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy, 8, 91. http://doi.org/10.3389/fnana.2014.00091 

⁃ Pyka-Pam-Utils--2014

This is a module with some helpful functions to process the data generated by PAM

License: GNU GPL v2.0
DOI: 10.5281/zenodo.3298825
PUBLICATIONS: Pyka, M., Klatt, S., & Cheng, S. (2014). Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy8, 91. http://doi.org/10.3389/fnana.2014.00091

Neural Networks (empty)

Cognitive Models

⁃ Dynamics of Disease States in Depression 

Major depressive disorder (MDD) is a disabling condition that adversely affects a person general health, work or school life, sleeping and eating habits, and person's family. Despite intense research efforts, the response rate of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. To advance our understanding of MDD, we use computational modelling as described in our article.

The model to simulate the dynamics of disease states in depression can be found below.

License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299247
PUBLICATIONS: Demic, S. & Cheng, S. (2014): Modeling the Disease States in Depression. PLoS ONE 9(10): e110358. https://doi.org/10.1371/journal.pone.0110358

⁃ Episodic Memory Deficits in Depression 

License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299871
PUBLICATIONS: Fang, J., Demic, S., & Cheng, S. (2018) The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory, PLOS ONE, 13(6), e0198406

Reinforcement Learning (empty)

High-quality figures

Our group aims to provide neuroscientific community with a collection of high-quality SVG-figures for free use in publications, presentations, websites etc. via GitHub.

All SVG-files in the repository are distributed under the terms of the Create Commons Attribution 4.0 International License.

Current available images include:

Dynamics of extinction learning in behavior and neural activity

How does learning unfold over time? This question can be studied in experimental data and with computational modeling. We analyze behavioral and neural activity data that was collected by collaborating labs. In our theoretical work, we employ simple associative models as well as deep reinforcement learning, which allows us to study the emerging representations and to correlate them to experimental data.

Emergent behavior and neural representations in spatial learning

Spatial navigation might appear to be a simple behavior, but closer inspection reveals that it is the complex result of many interacting sub-processes. We use deep reinforcement learning to understand how goal-directed behavior emerges in an artificial agent, how the deep neural network represents spatial information, and how the model's representations are related to neural codes for space in the hippocampus.

Nature and function of episodic memory

There is still great uncertainty as to what episodic memory is and what function it might serve. Within the research unit FOR 2812, we are working on an interdisciplinary framework for episodic memory.

Neural mechanisms underlying spatial navigation

A large number of cell types in the mammalian brain code for various types of spatial information, e.g., head direction cells, place cells, and grid cells. We study how networks of these cell types could support spatial navigation by combining computational modeling and data analysis.

Sequence memory in the hippocampus

The CRISP theory suggests that episodic memories are best represented by neuronal sequences and specific mechanisms by which sequences are stored and retrieved from the hippocampal circuit. Using neural network models, we investigate under which conditions the hippocampal circuit can perform the hypothesized functions reliably and robustly.

    in press

  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze "Mensch". Diskurse des Transhumanismus. Bielefeld: transcript Verlag
  • 2023

  • Working memory performance is tied to stimulus complexity
    Pusch, R., Packheiser, J., Azizi, A. H., Sevincik, C. S., Rose, J., Cheng, S., et al.
    Communications Biology, 6(1)
  • Tunable synaptic working memory with volatile memristive devices
    Ricci, S., Kappel, D., Tetzlaff, C., Ielmini, D., & Covi, E.
    Neuromorphic Computing and Engineering, 3(4), 044004
  • A map of spatial navigation for neuroscience
    Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.
    Neuroscience & Biobehavioral Reviews, 152, 105200
  • A Multisession SLAM Approach for RatSLAM
    Menezes, M., Muñoz, M., de Freitas, E. P., Cheng, S., de Almeida Neto, A., Ribeiro, P., & Oliveira, A.
    Journal of Intelligent & Robotic Systems, 108(4)
  • Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3
    de Jong, L. W., Nejad, M. M., Yoon, E., Cheng, S., & Diba, K.
    Current Biology, 33(9), 1689–1703.e5
  • Learning to predict future locations with internally generated theta sequences
    Parra-Barrero, E., & Cheng, S.
    PLOS Computational Biology, 19(5), e1011101
  • Navigation and the efficiency of spatial coding: insights from closed-loop simulations
    Ghazinouri, B., Nejad, M. M., & Cheng, S.
    Brain Structure and Function
  • A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning
    Diekmann, N., & Cheng, S.
    eLife, 12, e82301
  • CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning
    Diekmann, N., Vijayabaskaran, S., Zeng, X., Kappel, D., Menezes, M. C., & Cheng, S.
    Frontiers in Neuroinformatics, 17
  • Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors
    Siestrup, S., Jainta, B., Cheng, S., & Schubotz, R. I.
    Journal of Cognitive Neuroscience, 35(2), 291–313
  • Modeling the function of episodic memory in spatial learning
    Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S.
    Frontiers in Psychology, 14
  • 2022

  • xRatSLAM: An Extensible RatSLAM Computational Framework
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., de Almeida Neto, A., & de Oliveira, A. C. M.
    Sensors, 22(21), 8305
  • Navigation task and action space drive the emergence of egocentric and allocentric spatial representations
    Vijayabaskaran, S., & Cheng, S.
    PLOS Computational Biology, 18(10), e1010320
  • Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm
    Zöllner, C., Klein, N., Cheng, S., Schubotz, R. I., Axmacher, N., & Wolf, O. T.
    Quarterly Journal of Experimental Psychology, 174702182211166
  • A Model of Semantic Completion in Generative Episodic Memory
    Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.
    Neural Computation, 34(9), 1841–1870
  • Learning shifts the preferred theta phase of gamma oscillations in CA1
    Rayan, A., Donoso, J. R., Mendez-Couz, M., Dolón, L., Cheng, S., & Manahan-Vaughan, D.
    Hippocampus, 32(9), 695–704
  • The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study
    Batsikadze, G., Diekmann, N., Ernst, T. M., Klein, M., Maderwald, S., Deuschl, C., et al.
    NeuroImage, 253, 119080
  • What Happened When? Cerebral Processing of Modified Structure and Content in Episodic Cueing
    Siestrup, S., Jainta, B., El-Sourani, N., Trempler, I., Wurm, M. F., Wolf, O. T., et al.
    Journal of Cognitive Neuroscience, 34(7), 1287–1305
  • Learning Cognitive Map Representations for Navigation by Sensory–Motor Integration
    Zhao, D., Zhang, Z., Lu, H., Cheng, S., Si, B., & Feng, X.
    IEEE Transactions on Cybernetics, 52(1), 508–521
  • 2021

  • A multistage retrieval account of associative recognition ROC curves
    Hakobyan, O., & Cheng, S.
    Learning and Memory, 28(11), 400–404
  • Emergence of complex dynamics of choice due to repeated exposures to extinction learning
    Donoso, J. R., Packheiser, J., Pusch, R., Lederer, Z., Walther, T., Uengoer, M., et al.
    Animal Cognition, 24(6), 1279–1297
  • Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making
    Hakobyan, O., & Cheng, S.
    Journal of Cognitive Neuroscience, 33(6), 1032–1055
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
  • Self-referential false associations: A self-enhanced constructive effect for verbal but not pictorial stimuli
    Wang, J., Otgaar, H., Howe, M. L., & Cheng, S.
    Quarterly Journal of Experimental Psychology, 74(9), 1512–1524
  • Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal
    Packheiser, J., Donoso, J. R., Cheng, S., Güntürkün, O., & Pusch, R.
    Progress in Neurobiology, 197, 101901
  • Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach
    Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S.
    Scientific Reports, 11(1)
  • Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
    Parra-Barrero, E., Diba, K., & Cheng, S.
    eLife, 10, e70296
  • 2020

  • Improving sensory representations using episodic memory
    Görler, R., Wiskott, L., & Cheng, S.
    Hippocampus, 30(6), 638–656
  • Automatic Tuning of RatSLAM′s Parameters by Irace and Iterative Closest Point
    Menezes, M. C., Muñoz, M. E. S., Freitas, E. P., Cheng, S., Walther, T., Neto, A. A., et al.
    In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 562–568)
  • 2019

  • Hippocampal Reactivation Extends for Several Hours Following Novel Experience
    Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S., & Diba, K.
    The Journal of Neuroscience, 39(5), 866–875
  • Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia)
    Azizi, A. H., Pusch, R., Koenen, C., Klatt, S., Bröcker, F., Thiele, S., et al.
    Behavioural Brain Research, 356, 423–434
  • A Parallel RatSlam C++ Library Implementation
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Neto, A., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In Communications in Computer and Information Science (pp. 173–183) Springer International Publishing
  • Deep reinforcement learning in a spatial navigation task: Multiple contexts and their representation
    Diekmann, N., Walther, T., Vijayabaskaran, S., & Cheng, S.
    In 2019 Conference on Cognitive Computational Neuroscience Berlin, Germany: Cognitive Computational Neuroscience
  • How do memory modules differentially contribute to familiarity and recollection?
    Hakobyan, O., & Cheng, S.
    Behavioral and Brain Sciences, 42, e288
  • A Hippocampus Model for Online One-Shot Storage of Pattern Sequences
    Melchior, J., Bayati, M., Azizi, A., Cheng, S., & Wiskott, L.
    CoRR e-print arXiv:1905.12937
  • 2018

  • A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems
    Menezes, M. C., de Freitas, E. P., Cheng, S., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Autonomous Exploration Guided by Optimisation Metaheuristic
    Santos, R. G., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., & de Oliveira, A. C. M.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Storage fidelity for sequence memory in the hippocampal circuit
    Bayati, M., Neher, T., Melchior, J., Diba, K., Wiskott, L., & Cheng, S.
    PLOS ONE, 13(10), e0204685
  • The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory
    Fang, J., Demic, S., & Cheng, S.
    PLOS ONE, 13(6), e0198406
  • The Interaction between Semantic Representation and Episodic Memory
    Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.
    Neural Computation, 30(2), 293–332
  • Unsupervised Acquisition of Human Body Models
    Walther, T., & Würtz, R. P.
    Cognitive Systems Research, 47, 68–84
  • Doing without metarepresentation: Scenario construction explains the epistemic generativity and privileged status of episodic memory
    Werning, M., & Cheng, S.
    Behavioral and Brain Sciences, 41, e34
  • 2017

  • Unsupervised Acquisition of Human Body Models using Principles of Organic Computing
    Walther, T., & Würtz, R. P.
    ArXiv e-prints
  • Generating sequences in recurrent neural networks for storing and retrieving episodic memories
    Bayati, M., Melchior, J., Wiskott, L., & Cheng, S.
    In Proc. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
  • Consolidation of Episodic Memory: An Epiphenomenon of Semantic Learning
    Cheng, S.
    In N. Axmacher & Rasch, B. (Eds.), Cognitive Neuroscience of Memory Consolidation (pp. 57–72) Cham, Switzerland: Springer International Publishing
  • From grid cells to place cells with realistic field sizes
    Neher, T., Azizi, A. H., & Cheng, S.
    PLoS ONE, 12(7), e0181618
  • Taxonomy and Unity of Memory
    Werning, M., & Cheng, S.
    In S. Bernecker & Michaelian, K. (Eds.), The Routledge Handbook of Philosophy of Memory (pp. 7–20) New York: Routledge
  • 2016

  • Topological Schemas of Cognitive Maps and Spatial Learning
    Babichev, A., Cheng, S., & Dabaghian, Y. A.
    Frontiers in Computational Neuroscience, 10, 18
  • What is episodic memory if it is a natural kind?
    Cheng, S., & Werning, M.
    Synthese, 193(5), 1345–1385
  • Dissociating memory traces and scenario construction in mental time travel
    Cheng, S., Werning, M., & Suddendorf, T.
    Neuroscience & Biobehavioral Reviews, 60, 82–89
  • 2015

  • Self-organization of synchronous activity propagation in neuronal networks driven by local excitation
    Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S.
    Frontiers in Computational Neuroscience, 9, 69
  • Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics
    Neher, T., Cheng, S., & Wiskott, L.
    PLoS Computational Biology, 11(5), e1004250
  • 2014

  • Modeling the Dynamics of Disease States in Depression
    Demic, S., & Cheng, S.
    PLOS ONE, 9(10), 1–14
  • The transformation from grid cells to place cells is robust to noise in the grid pattern
    Azizi, A. H., Schieferstein, N., & Cheng, S.
    Hippocampus, 24(8), 912–919
  • Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections
    Pyka, M., Klatt, S., & Cheng, S.
    Frontiers in Neuroanatomy, 8, 91
  • Is Episodic Memory a Natural Kind?-A Defense of the Sequence Analysis
    Werning, M., & Cheng, S.
    In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (Vol. 2, pp. 964–69) Austin, TX: Cognitive Science Society
  • 2013

  • Composition and replay of mnemonic sequences: The contributions of REM and slow-wave sleep to episodic memory
    Cheng, S., & Werning, M.
    Behavioral and Brain Sciences, 36(06), 610–611
  • A computational model for preplay in the hippocampus
    Azizi, A. H., Wiskott, L., & Cheng, S.
    Frontiers in Computational Neuroscience, 7, 161
  • The CRISP theory of hippocampal function in episodic memory
    Cheng, S.
    Frontiers in Neural Circuits, 7, 88
  • Identification of two forebrain structures that mediate execution of memorized sequences in the pigeon
    Helduser, S., Cheng, S., & Güntürkün, O.
    Journal of Neurophysiology, 109(4), 958–968
  • 2012

  • Effect of synaptic plasticity on the structure and dynamics of disordered networks of coupled neurons
    Bayati, M., & Valizadeh, A.
    Phys. Rev. E, 86(1), 011925
  • Constraints on the synchronization of entorhinal cortex stellate cells
    Crotty, P., Lasker, E., & Cheng, S.
    Phys. Rev. E, 86(1), 011908
  • 2011

  • Reactivation, Replay, and Preplay: How It Might All Fit Together
    Buhry, L., Azizi, A. H., & Cheng, S.
    Neural Plasticity, 2011, 1–11
  • The structure of networks that produce the transformation from grid cells to place cells
    Cheng, S., & Frank, L. M.
    Neuroscience , 197, 293–306
  • 2008

  • New Experiences Enhance Coordinated Neural Activity in the Hippocampus
    Cheng, S., & Frank, L. M.
    Neuron , 57(2), 303–313
  • 2007

  • Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics
    Cheng, S., & Sabes, P. N.
    Journal of Neurophysiology, 97(4), 3057–3069
  • 2006

  • Modeling Sensorimotor Learning with Linear Dynamical Systems
    Cheng, S., & Sabes, P. N.
    Neural Computation, 18(4), 760–793
  • 2004

  • Statistical and dynamic models of charge balance functions
    Cheng, S., Petriconi, S., Pratt, S., Skoby, M., Gale, C., Jeon, S., et al.
    Phys. Rev. C, 69(5), 054906
  • 2003

  • Removing distortions from charge balance functions
    Pratt, S., & Cheng, S.
    Phys. Rev. C, 68(1), 014907
  • Isospin fluctuations from a thermally equilibrated hadron gas
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 67(4), 044904
  • 2002

  • Statistical physics in a finite volume with absolute conservation laws
  • Modeling Relativistic Heavy Ion Collisions
  • Effect of finite-range interactions in classical transport theory
    Cheng, S., Pratt, S., Csizmadia, P., Nara, Y., Molnár, D., Gyulassy, M., et al.
    Phys. Rev. C, 65(2), 024901
  • 2001

  • Quantum corrections for pion correlations involving resonance decays
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 63(5), 054904

    2023

  • Injecting noise into a spiking neural network model of spatial learning to induce exploration
    Buhaiev, A.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Modeling the targeted manipulation of behavior with reinforcement learning
    Gerecke, J. T.
    Bachelor's thesis, IT Security, Ruhr University Bochum, Germany
  • Comparing recurrent and feed-forward reinforcement learning agents in a spatial working memory task
    Forchap, W.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Comparison of Monte Carlo tree search with a MiniMax algorithm in perfect information games
    Kröcker, B.
    Computer Science, Ruhr University Bochum, Germany
  • Studying the role of grid cell properties in spatial navigation with spiking neural networks
    Boughedda, I.
    Applied Computer Science, Ruhr University Bochum, Germany
  • 2022

  • Analyses of emerging representations in Supervised Learning of spatial tasks with Convolutional Neural Networks
    Mousavi, S.
    Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Learning hippocampal theta sequences with behavioral timescale synaptic plasticity
    Kerscher, M.
    Applied Computer Science, Ruhr University Bochum, Germany
  • A model of hippocampal theta sequences based on phase precession and behavioral time scale plasticity
    Tomashevskaya, A.
    Applied Computer Science, Ruhr University Bochum, Germany
  • The Influence of Stimulus Representations on Recognition Memory
    Pronoza, J.
    Master’s thesis, Medical Physics, TU Dortmund, Germany
  • Anwendung eines Objektalgorithmus auf Textblöcken
    Markl, S.
    Applied Computer Science, Ruhr University Bochum, Germany
  • 2021

  • Spatial Representations in an Egocentric Navigation Task using Deep Reinforcement Learning
    Al-Dilaimi, S.
    Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Training a deep reinforcement learning agent with prioritized experience replay on navigation tasks
    Kellermann, N.
    Bachelor's thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Understanding the effects of place field size in spatial navigation using a spiking neural network
    Sens, Y.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Simulation of Spatial Behaviour using Spiking Neural Network
    Murugan, K. V.
    Master’s thesis, Computational Engineering, Ruhr University Bochum, Germany
  • Training State-Transition and Reward Models in Deep Neural Networks for Reinforcement Learning
    Altamimi, A.
    Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Generative effects of extinction learning emerging from an embodied associative network
    Lazarova, S.
    Master’s thesis, Cognitive Science, Ruhr University Bochum, Germany
  • 2020

  • Comparing Deep Reinforcement Learning Algorithms in Discrete and Continuous Environments
    De Stefano, G.
    Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Dynamical decoding of oscillatory neural activity during extinction learning using artificial neural networks
    Ghazinouri, B.
    Master’s thesis, Physics, Ruhr University Bochum, Germany
  • Deep Reinforcement Learning in Virtual Reality Environments Driven by Physics Simulation
    Woltersdorf, P.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Exploration of a continuous attractor model of grid cells with aligned conjunctive cells.
    Atak, M.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Spiking Network Model Of Hippocampus To Store And Recall Individual Patterns
    Bharadwai, M.
    Master’s thesis, Cognitive Systems, University of Ulm, Germany
  • Representational similarity of naturalistic stimuli from different categories in a deep neural network
    Simon, R.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Learning pose-invariant object representations using Power-SFA
    van Wickern, J.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • 2019

  • Differential Visual Discrimination Performance Dependent on Stimulus Category in a Deep Neural Network Model of the Visual Cortex
    Boschuk, V.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Simulating the effects of the balance between excitatory and inhibitory neurons on the dynamics of spiking neural networks
    Droßel, A.
    Master’s thesis, Physics, TU Dortmund, Germany
  • Reinforcement learning in multiple contexts: How does the network represent the learned information?
    Diekmann, N.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany
  • Transfer of Learned Associations in a Reinforcement Learning Context
    Vijayabaskaran, S.
    Master’s thesis, Computational Engineering, Univ. of Bochum, Germany
  • Driving network oscillations in ING and PING models with realistic spiking inputs
    Sowade, J.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany
  • 2018

  • Storing and retrieving neural sequences in a network model of the hippocampus with spiking neurons and STDP
    Dyck, S.
    Master’s thesis, Cognitive Science, Univ. of Bochum, Germany
  • Spontaneous sequence generation in a neural network model of the hippocampus
    Matzke, S.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • 2017

  • Generating Neural Network Connectivity from a Visual Representation
    Herbers, P.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany
  • 2016

  • Propagation of sequential activity in feedforward neural networks
    Hakobyan, O.
    Master’s thesis, Cognitive Science, Univ. of Bochum, Germany
  • Uncovering the representation of visual categories in neural ensembles
    Klatt, S.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Modeling the effect of depression on mortality.
    Ogiermann, D.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Using Kalman filtering to combine path integration and external spatial information in a mobile robot
    Diekmann, N.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • 2015

  • Modeling and analyzing depression using finite state models
    Henning, H.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Integration realistischer Dendritenmorphologien in der Modellierungsumgebung PAM
    Herbers, P.
    Applied Computer Science, Ruhr University Bochum, Germany
  • Structural and Functional Analysis of the Pigeon Hippocampus
    Görler, R.
    Master’s thesis, Applied Computer Science, Ruhr University Bochum, Germany
  • Allometric and behavioral factors of hippocampal theta frequency
    Kill, L.
    Psychology, Ruhr University Bochum, Germany
  • Einfluss von Ortsparametern auf Evolvabilität und Komplexität in Phenotypen einer Open-End Evolutionssimulation
    Sowade, J.
    Applied Computer Science, Ruhr University Bochum, Germany
  • 2014

  • What I don’t know won’t hurt me. Ambiguous decisions compared to risky ones under the influence of stress
    Buschmüller, T.
    Psychology, Ruhr University Bochum, Germany
  • Entwicklung eines ePuck-Simulators für Blender.
    Rutte, S.
    Applied Computer Science, Ruhr University Bochum, Germany

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210