Ferrari, Ulisse ; Deny, Stephane ; Chalk, Matthew J ; Tkacik, GasperIST Austria ; Marre, Olivier ; Mora, Thierry
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.
Physical Review E
This work was supported by ANR Trajectory, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche (LIFESENSES; ANR-10-LABX-65), EC Grant No. H2020-785907 from the Human Brain Project, NIH Grant No. U01NS090501, and an AVIESAN-UNADEV grant to O.M. M.C. was supported by the Agence Nationale de la Recherche Jeune Chercheur/Jeune Chercheuse grant (ANR-17-CE37-0013).
Ferrari U, Deny S, Chalk MJ, Tkacik G, Marre O, Mora T. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 2018;98(4). doi:10.1103/PhysRevE.98.042410
Ferrari, U., Deny, S., Chalk, M. J., Tkacik, G., Marre, O., & Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E, 98(4). https://doi.org/10.1103/PhysRevE.98.042410
Ferrari, Ulisse, Stephane Deny, Matthew J Chalk, Gasper Tkacik, Olivier Marre, and Thierry Mora. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E 98, no. 4 (2018). https://doi.org/10.1103/PhysRevE.98.042410.
U. Ferrari, S. Deny, M. J. Chalk, G. Tkacik, O. Marre, and T. Mora, “Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons,” Physical Review E, vol. 98, no. 4, 2018.
Ferrari U, Deny S, Chalk MJ, Tkacik G, Marre O, Mora T. 2018. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 98(4).
Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:10.1103/PhysRevE.98.042410.
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