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

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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2022

  1. Luque NR, Naveros F, Sheynikhovich D, Ros E and Arleo A (2022) Computational epidemiology study of homeostatic compensation during sensorimotor aging. Neural Networks, 146:316-333.

2020

  1. Bécu M, Sheynikhovich D, Ramanoël S, Tatur G, Ozier-Lafontaine A, Sahel J-A and Arleo A (2020) Modulation of spatial cue processing across the lifespan: a geometric polarization of space restores allocentric navigation strategies in children and older adults. bioRxiv.
  2. Bécu M, Sheynikhovich D, Tatur G, Agathos C, Bologna LL, Sahel JA and Arleo A (2020) Age-related preference for geometric spatial cues during real-world navigation. Nature Human Behaviour, 4(1):88-99.
  3. Li T, Arleo A and Sheynikhovich D (2020) A model of a panoramic visual representation in the dorsal visual pathway: the case of spatial reorientation and memory-based search. bioRxiv.
  4. Li T, Arleo A and Sheynikhovich D (2020) Modeling place cells and grid cells in multi-compartment environments: hippocampal-entorhinal loop as a multisensory integration circuit. Neural Networks, 121:37-51.

2019

  1. Li T, Arleo A and Sheynikhovich D (2019) Modeling place cells and grid cells in multi-compartment environments: hippocampal-entorhinal loop as a multisensory integration circuit. bioRxiv.

2018

  1. 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.

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. 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 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. 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.
  2. 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.

2009

  1. 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.

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. 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.
  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. Chavarriaga R, Strösslin T, Sheynikhovich D and Gerstner W (2005) A Computational Model of Parallel Navigation Systems in Rodents. Neuroinformatics, 3(3):223-242.

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.