Dr. Denis Sheynikhovich (Associate Professor Sorbonne Université)

Vision Institute
Aging in Vision and Action Lab
CNRS – INSERM – University Pierre&Marie Curie
17, rue Moreau F-75012 Paris, France
Phone: +33 (0)1 53 46 26 55


My research work focuses on computational modeling of neural mechanisms supporting spatial memory and behavior. My main subjects of interest are the following:

Linking neural activity in the hippocampal formation and spatial behavior.

The hippocampal formation contains different types of neurons, the activity of which is highly correlated with spatial location of a navigating animal. Is it true that the activity of these neurons represents the animal’s memory about where it is? If yes, then behavioral decisions of this animal must be determined (at least in part) by the activity of those neurons. I am interested in reconciling the neural data and behavior of animals by means of computational modeling. In my doctoral thesis, a system-level model of rodent navigation has been proposed that provided a link between firing properties of location-sensitive neurons in the hippocampal formation and behavioral decisions made by rats in several spatial navigation tasks [1]. Main contributions of this work concern the interaction between grid cells and place cells in the hippocampal formation, implementation of different navigational strategies and the existence of a `geometric module‘ in the rat’s brain.

Implementation of navigational strategies in the brain

Goal-oriented spatial behavior can be expressed in a number of different navigational strategies. These are, for example, simple stimulus-response behaviors such as approaching a landmark, and more complex strategies involving mental mapping and planning. Different memory representations support those strategies, e.g. visual memory of a landmark is sufficient to approach it, whereas a mental map, potentially combining many landmarks and their spatial relations, is required to plan a trajectory. The question I am interested in is how a suitable strategy (and the corresponding memory representation) is chosen depending on task requirements? We have proposed a simple model of strategy switching, in which strategies are chosen based on their past performance [2,3]. According to some experimental evidence, the prefrontal cortex is responsible for evaluation of different strategies and switching between them.

The role of neuromodulator dopamine for long-term memory in the prefrontal cortex

In order to learn a goal-oriented strategy and use it efficiently, a long-term memory is required. According to to a large body of evidence, goal information is delivered to various brain areas by neuromodulator dopamine. I am interested in the neural mechanisms of dopamine influence on long-term memory in prefrontal cortex neurons [4]. In particular, on the basis of neuro-physiological studies we proposed a model of synaptic plasticity in prefrontal cortex neurons under the influence of dopamine.

The role of aging in visual information processing during spatial navigation

The new focus of my work concerns the influence of neural aging on the brain’s spatial navigation network. Via a variety of neural processing stages visual information from the retina arrives to the hippocampal formation, where  it is combined with other multi-modal sensory signals to produce an internal spatial representation. Even in a healthy individual the quality of such a representation gets worse with age. What is the contribution of purely visual aging in this age-related impairment ? Can this impairment be explained by age-related changes in synaptic plasticity, as it seems to be the case in rodents ? What could be the role of neuronal noise in this impairment ? In the recently created laboratory at the Vision Institute we will try to address these questions by combining computational modeling, psychophysical and behavioral experiments in humans.

Keywords: spatial memory, spatial behavior, neural networks, computational modeling, spiking neurons, learning and synaptic plasticity, place cells, cognitive map, reinforcement learning, vision, healthy aging.


Google scholar

Thomson reuters (Researcher ID K-3776-2013)


CV (pdf): [ download ]

Thesis (pdf): [ download ]

List of main publication (pdf): [ download ]


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  1. Sheynikhovich D, Grèzes F, King J-R and Arleo A (2012) Exploratory behaviour depends on multisensory integration during spatial learning. In LNCS - Artificial Neural Networks and Machine Learning, vol. 7552, pages 296-303, Springer.


  1. Dollé L, Sheynikhovich D, Girard B, Ujfalussy B, Chavarriaga R and Guillot A (2010) Analyzing interactions between cue-guided and place-based navigation with a computational model of action selection: Influence of sensory cues and training. In Doncieux, S. et al., editors, LNAI - Simulation of Adaptive Behavior, vol. 6226, pages 335-346, Springer-Verlag.
  2. Sheynikhovich D, Dolle L, Chavarriga R and Arleo A (2010) Minimal model of strategy switching in the plus-maze navigation task. In Doncieux, S. et al., editors, LNAI - Simulation of Adaptive Behavior, vol. 6226, pages 390-401, Springer-Verlag.
  3. Sheynikhovich D, Otani S and Arleo A (2010) A modeling study of the role of tonic vs. phasic dopamine input to the prefrontal cortex. In Gervais, R. et al., editors, Proceedings of the Fifth french conference on computational neuroscience (Neurocomp), pages 77-81.


  1. Sheynikhovich D, Otani S and Arleo A (2009) Role of dopamine for long-term plasticity in the rat prefrontal cortex: a computational model. In Renaud, S. and Saighi, S., editors, Proceedings of the Fourth french conference on computational neuroscience (Neurocomp), vol. 4, pages 30.
  2. Martinet L-E, Sheynikhovich D and Arleo A (2009) A cortical column model for studying spatial navigation planning. In Proceedings of the Fourth french conference on computational neuroscience (Neurocomp), vol. 4, pages 24, Bordeaux, France.
  3. Passot J-B, Arabo A, Sheynikhovich D, Rondi-Reig L and Arleo A (2009) Studying the role of the cerebellum in spatial cognition through a neurocomputational approach. In Renaud, S. and Saighi, S., editors, Proceedings of the Fourth french conference on computational neuroscience (Neurocomp), vol. 4, pages 26.


  1. Lukšys G, Knüsel J, Sheynikhovich D, Sandi C and Gerstner W (2007) Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning. In B. Schölkopf and J. Platt and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 937-944, MIT Press, Cambridge, MA.


  1. Sheynikhovich D, Chavarriaga R, Strosslin T and Gerstner W (2005) Spatial Representation and Navigation in a Bio-inspired Robot. In S. Wermter and G. Palm and M. Elshaw, editors, Biomimetic Neural Learning for Intelligent Robots, vol. 3575, pages 245-264, Springer-Verlag GmbH, Lecture Notes in Artificial Intelligence 3575.
  2. Strösslin T, Chavarriaga R, Sheynikhovich D and Gerstner W (2005) Modelling path integrator recalibration using hippocampal place cells. In W. Duch and J. Kacprzyk and E. Oja, editors, Artificial Neural Networks: Biological Inspirations - ICANN 2005, Part I, pages 51-56, Springer-Verlag, Berlin Heidelberg, LNCS 3696 .


  1. Gurov IP and Sheynikhovich DV (1996) Noise-immune phase-shifting interferometric system based on Markov nonlinear filtering method. In Dougherty, Edward. R and Preteux, Francoise and Davidson, Jennifer L., editors, Proc SPIE Vol. 2823, Stat. Stoch. Methods Image Process., vol. 2823, pages 121-125, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series.