Dr. Denis Sheynikhovich (Associate Professor UPMC)

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

email:

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.

Bibliometry

Google scholar

Thomson reuters (Researcher ID K-3776-2013)

 

CV (pdf): [ download ]

Thesis (pdf): [ download ]

List of main publication (pdf): [ download ]

Publications

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2018

  1. Bécu M, Tatur G, Sheynikhovich D, Ramanoel S, Agathos C and Arleo A (2018) Age-related preference for geometric cues during real-world navigation: behavioral and neuroimaging correlates. In Presentation at the Interdisciplinary Navigation Symposium (iNav), Mont-Tremblant, Canada.
  2. Bécu M, Tatur G, Sheynikhovich D, Agathos C, Bologna LL and Arleo A (2018) Age-related preference for geometric cues during real-world navigation. In Poster presented at Forum of Neuroscience (FENS), Berlin, Germany, pages 345.
  3. Bécu M, Tatur G, Sheynikhovich D, Ramanoel S, Agathos C and Arleo A (2018) Age-related preference for geometric cues during real-world navigation: behavioral and neuroimaging correlates. In Presentation at Spatial Cognition - Doctoral Symposium, Tübingen, Germany.
  4. Sheynikhovich D, Bécu M, Wu C and Arleo A (2018) Unsupervised detection of microsaccades in high-noise regime. Journal of Vision, 18(6):1-16.
  5. Li T, Arleo A and Sheynikhovich D (2018) Bidirectional interactions between place-cells and grid-cells in the vision- and self-motion driven spatial representation model. In 11th FENS Forum of Neuroscience, Berlin, Germany.
  6. Li T, Arleo A and Sheynikhovich D (2018) Vision-based model of primate spatial cognition: coordinate transformations, limited visual field and reorientation. In 2nd Interdisciplinary Navigation Symposium, iNAV 2018, Quartier Tremblant, Canada.

2017

  1. Li T, Arleo A and Sheynikhovich D (2017) Coordinate-transformation spiking neural network for spatial navigation. In HBP 2017: Collaborative and Integrative Modeling of Hippocampus, Paris, France.
  2. Li T, Arleo A and Sheynikhovich D (2017) Coordinate-transformation spiking neural network for spatial navigation. In 3e Symposium des Neurosciences Computationnelles de l'UPMC, Paris, France.
  3. Li T, Arleo A and Sheynikhovich D (2017) Coordinate-transformation spiking neural network for spatial navigation. In 26th Annual Computational Neuroscience Meeting (CNS2017), vol. 18 (Suppl), pages P239, BMC Neuroscience 18 (Suppl).
  4. Arleo A, Becu M, Tatur G, DeDieuleveult A, Wu C, Marchesotti S and Sheynikhovich D (2017) Effect of aging on ocular fixation and microsaccades during optic flow. In Journal of vision, vol. 17(10), pages 890.
  5. Bécu M, Tatur G, Bourefis A, Sheynikhovich D and Arleo A (2017) Age-related changes in gaze dynamics during real-world navigation. In Poster session presented at European Conference on Eye Movements, Wuppertal, Germany.
  6. Bécu M, Tatur G, DeDieuleveult A, Wu C, Marchesotti S, Sheynikhovich D and Arleo A (2017) Effect of aging on ocular fixation and microsaccades during optic flow. In Poster session presented at European Conference on Eye Movements, Wuppertal, Germany.
  7. Bécu M, Tatur G, Bourefis A, Bologna LL, Sheynikhovich D and Arleo A (2017) Age-related changes in gaze dynamics during real-world navigation. In Journal of Vision, vol. 17(10), pages 540.

2013

  1. Sheynikhovich D, Otani S and Arleo A (2013) Dopaminergic control of LTD/LTP threshold in prefrontal cortex. Journal of Neuroscience, 33(34):13914-26.

2012

  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.
  2. Sheynikhovich D, Otani S and Arleo A (2012) The role of dopamine in LTP/LTD threshold modulation in the prefrontal cortex. In 8th FENS Forum of Neuroscience, Barcelona,Spain.
  3. Passot J-B, Sheynikhovich D, Duvelle E and Arleo A (2012) Contribution of cerebellar sensorimotor adaptation to hippocampal spatial memory. PLoS ONE, 7(4):e32560.

2011

  1. Sheynikhovich D, Otani S and Arleo A (2011) The role of tonic and phasic dopamine for long-term synaptic plasticity in the prefrontal cortex: a computational model. Journal of Physiology P, 105 (1-3):45-52.
  2. Martinet LE, Sheynikhovich D, Benchenane K and Arleo A (2011) Spatial learning and action planning in a prefrontal cortical network model. In Proceedings of the 10th French Society for Neuroscience Meeting, Marseille, France.
  3. Passot JB, Sheynikhovich D, Rondi-Reig L and Arleo A (2011) A neurocomputational study of the role of the cerebellum in spatial cognition. In Proceedings of the 10th French Society for Neuroscience Meeting, Marseille, France.
  4. Sheynikhovich D, Otani S and Arleo A (2011) The role of phasic and tonic dopamine for long-term plasticity in the rat prefrontal cortex: a computational model. In Proceedings of the 10th French Society for Neuroscience Meeting, Marseille, France.
  5. Martinet L-E, Sheynikhovich D, Benchenane K and Arleo A (2011) Spatial Learning and Action Planning in a Prefrontal Cortical Network Model. PLoS Comput Biol, 7(5):e1002045.

2010

  1. Sheynikhovich D and Arleo A (2010) A reinforcement learning approach to model interactions between landmarks and geometric cues during spatial learning. Brain Research, 1365:35-47.
  2. 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.
  3. Martinet L-E, Sheynikhovich D, Benchenane K and Arleo A (2010) Integrating a hippocampal and a cortical model for spatial navigation planning. In 14th International Conference on Cognitive and Neural Systems (ICCNS'10), Boston, USA.
  4. 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.
  5. Sheynikhovich D, Otani S and Arleo A (2010) The role of dopamine in long-term plasticity in the rat prefrontal cortex: a computational model. In Frontiers in Systems Neuroscience, Conference Abstract: Computational and Systems Neuroscience (Cosyne).
  6. Sheynikhovich D, Otani S and Arleo A (2010) The role of dopamine in long-term plasticity in the rat prefrontal cortex: a computational model. In FENS Abstracts, vol. 5.
  7. Dollé L, Sheynikhovich D, Girard B, Chavarriaga R and Guillot A (2010) Path planning versus cue responding: a bioinspired model of switching between navigation strategies. Biological Cybernetics, 103(4):299-317.
  8. 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.

2009

  1. Martinet L-E, Sheynikhovich D, Benchenane K and Arleo A (2009) Integrating a hippocampal and a cortical model for spatial navigation planning. In Fourth Computational Cognitive Neuroscience Conference (CCNC'09), Boston, USA.
  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. Martinet L-E, Sheynikhovich D, Meyer J-A and Arleo A (2009) Multimodal encoding in a cortical model for spatial navigation planning. In BMC Neuroscience - Eighteenth Annual Computational Neuroscience Meeting, vol. 10 (Suppl1), pages 338, Berlin, Germany.
  4. 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.
  5. Sheynikhovich D, Chavarriaga R, Strösslin T, Arleo A and Gerstner W (2009) Is there a geometric module for spatial orientation? Insights from a rodent navigation model. Psychological Review, 116(3):540-566.
  6. 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.
  7. Sheynikhovich D, Chavarriaga R, Strösslin T, Arleo A and Gerstner W (2009) Is There a Geometric Module for Spatial Orientation?: Insights From a Rodent Navigation Model. In Proceedings of the 4th Computational Cognitive Neuroscience Conf, Boston, USA.
  8. Martinet L-E, Sheynikhovich D, Meyer J-A and Arleo A (2009) A cortical model for spatial navigation planning. In Journées Francophones Planification Décision Apprentissage (JFPDA 2009), Paris, France.
  9. Martinet L-E, Sheynikhovich D, Meyer J-A and Arleo A (2009) Multimodal encoding in a cortical model for spatial navigation planning. In Colloque des Jeunes Chercheurs en Sciences Cognitives (CJCSC'09), Toulouse, France.

2007

  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.

2006

  1. Sheynikhovich D, Chavarriaga R, Strösslin T and Gerstner W (2006) Adaptive sensory processing for efficient place coding. Neurocomputing, 69(10-12):1211-1214.

2005

  1. Chavarriaga R, Strösslin T, Sheynikhovich D and Gerstner W (2005) A Computational Model of Parallel Navigation Systems in Rodents. Neuroinformatics, 3(3):223-242.
  2. Chavarriaga R, Strösslin T, Sheynikhovich D and Gerstner W (2005) Competition between cue response and place response : A model of rat navigation behaviour. Connection Science, 17(1-2):167-183.
  3. 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.
  4. 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 .
  5. Strösslin T, Sheynikhovich D, Chavarriaga R and Gerstner W (2005) Robust self-localisation and navigation based on hippocampal place cells. Neural Networks, 8(19):1125-1140.

2000

  1. Gurov I and Sheynikhovich D (2000) Interferometric data analysis based on Markov non-linear filtering methodology. Journal of the Optical Society of America A, 17(1):21-27.

1997

  1. Gurov I and Sheynikhovich D (1997) Calculating of phase characteristics of interferometric pattern by the method of Markov non-linear filtering. Optics and Spectroscopy, 83(1):147-152.