Skip to main content

Research Repository

Advanced Search

All Outputs (18)

Coordinated prefrontal state transition leads extinction of reward-seeking behaviors (2021)
Journal Article
Russo, E., Ma, T., Spanagel, R., Durstewitz, D., Toutounji, H., & Köhr, G. (2021). Coordinated prefrontal state transition leads extinction of reward-seeking behaviors. Journal of Neuroscience, 41(11), 2406-2419. https://doi.org/10.1523/jneurosci.2588-20.2021

Extinction learning suppresses conditioned reward responses and is thus fundamental to adapt to changing environmental demands and to control excessive reward seeking. The medial prefrontal cortex (mPFC) monitors and controls conditioned reward respo... Read More about Coordinated prefrontal state transition leads extinction of reward-seeking behaviors.

Coordinated prefrontal state transition leads extinction of reward-seeking behaviors (2020)
Other
Russo, E., Ma, T., Spanagel, R., Durstewitz, D., Toutounji, H., & Köhr, G. Coordinated prefrontal state transition leads extinction of reward-seeking behaviors

Extinction learning suppresses conditioned reward responses and is thus fundamental to adapt to changing environmental demands and to control excessive reward seeking. The medial prefrontal cortex (mPFC) monitors and controls conditioned reward respo... Read More about Coordinated prefrontal state transition leads extinction of reward-seeking behaviors.

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI (2019)
Journal Article
Koppe, G., Toutounji, H., Kirsch, P., Lis, S., & Durstewitz, D. (2019). Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. PLoS Computational Biology, 15(8), Article e1007263. https://doi.org/10.1371/journal.pcbi.1007263

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the ide... Read More about Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI.

Neural networks and neurocomputational modeling (2018)
Book Chapter
Toutounji, H., Hertäg, L., & Durstewitz, D. (2018). Neural networks and neurocomputational modeling. In . E. Wagenmakers (Ed.), Stevens' handbook of experimental psychology and cognitive neuroscience, Vol. V: Methodology. (4th edition). Wiley. https://doi.org/10.1002/9781119170174.epcn517

This chapter reviews methods of neurocomputational modeling, ranging from biophysically detailed single neuron and synapse models to connectionist?style, abstract network formalisms. These methods form an arsenal of mathematical tools that draw on dy... Read More about Neural networks and neurocomputational modeling.

Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings (2018)
Journal Article
Toutounji, H., & Durstewitz, D. (2018). Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings. Frontiers in Neuroinformatics, 12, Article 67. https://doi.org/10.3389/fninf.2018.00067

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points... Read More about Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings.

Models of Neural Homeostasis (2017)
Book Chapter
Toutounji, H. (2017). Models of Neural Homeostasis. In A. A. Moustafa (Ed.), Computational Models of Brain and Behavior (257-269). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119159193.ch19

Neurons can regulate their own excitability, either by modulating synaptic drive, or by adapting the somatic apparatus of spike generation. These homeostatic processes are thought to keep neural activity within healthy limits, and to stabilize the He... Read More about Models of Neural Homeostasis.

Computational models as statistical tools (2016)
Journal Article
Durstewitz, D., Koppe, G., & Toutounji, H. (2016). Computational models as statistical tools. Current Opinion in Behavioral Sciences, 11, 93-99. https://doi.org/10.1016/j.cobeha.2016.07.004

© 2016 Elsevier Ltd Traditionally, models in statistics are relatively simple ‘general purpose’ quantitative inference tools, while models in computational neuroscience aim more at mechanistically explaining specific observations. Research on methods... Read More about Computational models as statistical tools.

Autonomous Learning Needs a Second Environmental Feedback Loop (2015)
Book Chapter
Toutounji, H., & Pasemann, F. (2016). Autonomous Learning Needs a Second Environmental Feedback Loop. In Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013 (455-472). Springer International Publishing. https://doi.org/10.1007/978-3-319-23392-5_25

Deriving a successful neural control of behavior of autonomous and embodied systems poses a great challenge. The difficulty lies in finding suitable learning mechanisms, and in specifying under what conditions learning becomes necessary. Here, we pro... Read More about Autonomous Learning Needs a Second Environmental Feedback Loop.

Homeostatic plasticity for single node delay-coupled reservoir computing (2015)
Journal Article
Toutounji, H., Schumacher, J., & Pipa, G. (2015). Homeostatic plasticity for single node delay-coupled reservoir computing. Neural Computation, 27(6), 1159-1185. https://doi.org/10.1162/NECO_a_00737

© 2015 Massachusetts Institute of Technology. Supplementing a differential equation with delays results in an infinitedimensional dynamical system. This property provides the basis for a reservoir computing architecture, where the recurrent neural ne... Read More about Homeostatic plasticity for single node delay-coupled reservoir computing.

An Introduction to Delay-Coupled Reservoir Computing (2014)
Conference Proceeding
Schumacher, J., Toutounji, H., & Pipa, G. (2014). An Introduction to Delay-Coupled Reservoir Computing. In Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics (63-90). https://doi.org/10.1007/978-3-319-09903-3_4

Reservoir computing has been successfully applied in difficult time series prediction tasks by injecting an input signal into a spatially extended reservoir of nonlinear subunits to perform history-dependent nonlinear computation. Recently, the netwo... Read More about An Introduction to Delay-Coupled Reservoir Computing.

Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations (2014)
Journal Article
Toutounji, H., & Pipa, G. (2014). Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations. PLoS Computational Biology, 10(3), https://doi.org/10.1371/journal.pcbi.1003512

It is a long-established fact that neuronal plasticity occupies the central role in generating neural function and computation. Nevertheless, no unifying account exists of how neurons in a recurrent cortical network learn to compute on temporally and... Read More about Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations.

Optimized Temporal Multiplexing for Reservoir Computing with a Single Delay-Coupled Node (2014)
Conference Proceeding
TOUTOUNJI, H., Schumacher, J., & Pipa, G. (2014). Optimized Temporal Multiplexing for Reservoir Computing with a Single Delay-Coupled Node. https://doi.org/10.15248/proc.1.519

The computational performance of reservoir computers based on a single delay-coupled node is critically dependent on the temporal multiplexing of input to the reservoir. Here we present an optimization of the temporal multiplexing by means of optimiz... Read More about Optimized Temporal Multiplexing for Reservoir Computing with a Single Delay-Coupled Node.

Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons (2014)
Journal Article
Toutounji, H., & Pasemann, F. (2014). Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons. Frontiers in Neurorobotics, 8, https://doi.org/10.3389/fnbot.2014.00019

The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuron... Read More about Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons.

An Analytical Approach to Single Node Delay-Coupled Reservoir Computing (2013)
Conference Proceeding
Schumacher, J., Toutounji, H., & Pipa, G. (2013). An Analytical Approach to Single Node Delay-Coupled Reservoir Computing. In Artificial Neural Networks and Machine Learning – ICANN 2013 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings (26-33). https://doi.org/10.1007/978-3-642-40728-4_4

Reservoir computing has been successfully applied in difficult time series prediction tasks by injecting an input signal into a spatially extended reservoir of nonlinear subunits to perform history-dependent nonlinear computation. Recently, the netwo... Read More about An Analytical Approach to Single Node Delay-Coupled Reservoir Computing.

Scalable reinforcement learning through hierarchical decompositions for weakly-coupled problems (2011)
Conference Proceeding
Toutounji, H., Rothkopf, C. A., & Triesch, J. (2011). Scalable reinforcement learning through hierarchical decompositions for weakly-coupled problems. In 2011 IEEE International Conference on Development and Learning (ICDL). https://doi.org/10.1109/devlrn.2011.6037351

Reinforcement Learning, or Reward-Dependent Learning, has been very successful at describing how animals and humans adjust their actions so as to increase their gains and reduce their losses in a wide variety of tasks. Empirical studies have furtherm... Read More about Scalable reinforcement learning through hierarchical decompositions for weakly-coupled problems.

On Randomness and the Genetic Behavior of Cellular Automata (2008)
Conference Proceeding
Toutounji, H., & Aljundi, A. C. (2008). On Randomness and the Genetic Behavior of Cellular Automata. In 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications. https://doi.org/10.1109/ICTTA.2008.4530320

We investigate a new approach in utilizing a given classification to cellular automata to search for a particular behavior cellular automaton with a genetic algorithm. This investigation leads to the formation of two new concepts. The first is creati... Read More about On Randomness and the Genetic Behavior of Cellular Automata.