Attractor neural networks and the resolution of a brain’s self-positioning system

Host laboratory and collaborators


Self-localization based on internal and/or external sensory information is essential to animal survival. The hippocampus plays a crucial role in this process by forming a mental map of the environment. The factors influencing internal spatial coding resolution are poorly understood. Recent large-scale in vivo recordings of hippocampal neuronal activity in mice navigating virtual reality environments show local variations in spatial coding resolution. They could result from genuine differences within a single internal map or possible switches between different maps with different spatial resolutions. To solve this issue, we propose to use inference models based on functional connectivity (Ising models) as a neural activity decoder to decipher at high speed the neuronal assemblies or maps in use. Map switching will be revealed as instabilities (or flickers). In a second step, we will build an attractor neuronal network model fed by both external and internal information to investigate mechanisms of spatial resolution modulation.


Decoding, Resolution, Attractor networks, Ising models, self-location, Hippocampus


Aim1: Develop a dual decoder allowing fast decoding of cell assemblies or maps associated with different reference frames (internal vs external) and encoded position from large-scale multi-electrode recordings of hippocampal neuron activity during spatial navigation in virtual reality environments.

Aim2: Build a Continuous Attractor Neuronal Network (CANN) model to investigate possible mechanisms involved in adaptive modulation of spatial coding resolution (inhomogeneous attractor or switch between different attractors).

Expected profile

The recruited post-doc while working at INMED, will hold a PhD in statistical physics, mathematics or computer science. He will develop and apply Ising models as developed in the Monasson lab and close to models developed in the Rouault lab to biological questions investigated in the Epsztein lab.

Continuation of an existing project

This project is an entirely new project aiming at modelling recent experimental results obtained in the Epsztein lab. Current attractor networks used to model self-location are unsuitable to model
inhomogeneities in spatial coding resolution. An important question of the current project is to decipher whether variations in spatial coding resolution reflect switches between homogeneous attractors each having different spatial coding resolution or a single but inhomogeneous attractor.

Articles related to the project

-Attractor dynamics of spatially correlated neural activity in the limbic system. Knierim JJ, Zhang K. Annu Rev Neurosci. 2012;35:267-85.
-Integration and multiplexing of positional and contextual information by the hippocampal network. Posani L, Cocco S, Monasson R. PLoS Comput Biol. 2018 Aug 14;14(8).
-Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by AttractorNeural Networks. Battista A, Monasson R. Phys Rev Lett. 2020 Jan 31;124(4):048302.
-Dynamic control of hippocampal spatial coding resolution by local visual cues. Bourboulou R, Marti G, Michon FX, El Feghaly E, Nouguier M, Robbe D, Koenig J, Epsztein J. Elife. 2019 Mar 1;8.
-Ring attractor dynamics in the Drosophila central brain. Kim SS, Rouault H, Druckmann S, Jayaraman V. Science. 2017 May 26;356(6340):849-853.