Journal of Computational Neuroscience

, Volume 41, Issue 3, pp 269–293 | Cite as

Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings



We present two graphical model-based approaches to analyse the distribution of neural activities in the prefrontal cortex of behaving rats. The first method aims at identifying cell assemblies, groups of synchronously activating neurons possibly representing the units of neural coding and memory. A graphical (Ising) model distribution of snapshots of the neural activities, with an effective connectivity matrix reproducing the correlation statistics, is inferred from multi-electrode recordings, and then simulated in the presence of a virtual external drive, favoring high activity (multi-neuron) configurations. As the drive increases groups of neurons may activate together, and reveal the existence of cell assemblies. The identified groups are then showed to strongly coactivate in the neural spiking data and to be highly specific of the inferred connectivity network, which offers a sparse representation of the correlation pattern across neural cells. The second method relies on the inference of a Generalized Linear Model, in which spiking events are integrated over time by neurons through an effective connectivity matrix. The functional connectivity matrices inferred with the two approaches are compared. Sampling of the inferred GLM distribution allows us to study the spatio-temporal patterns of activation of neurons within the identified cell assemblies, particularly their activation order: the prevalence of one order with respect to the others is weak and reflects the neuron average firing rates and the strength of the largest effective connections. Other properties of the identified cell assemblies (spatial distribution of coactivation events and firing rates of coactivating neurons) are discussed.


Cell assemblies Replay Statistical inference Ising model Generalized linear model 



This work is a follow-up of a previous study in collaboration with F.P. Battaglia and U. Ferrari (Tavoni et al. 2015), to whom we are very grateful.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Laboratoire de Physique Statistique, Ecole Normale Supérieure, CNRS, PSL ResearchSorbonne Université UPMCParisFrance
  2. 2.Laboratoire de Physique Théorique, Ecole Normale Supérieure, CNRS, PSL ResearchSorbonne Université UPMCParisFrance
  3. 3.Department of Physics & AstronomyUniversity of PennsylvaniaPhiladelphiaUSA

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