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Inferring Synaptic Structure in Presence of Neural Interaction Time Scales

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  • Cristiano Capone
  • Carla Filosa
  • Guido Gigante
  • Federico Ricci-Tersenghi
  • Paolo Del Giudice

Abstract

Biological networks display a variety of activity patterns reflecting a web of interactions that is complex both in space and time. Yet inference methods have mainly focused on reconstructing, from the network’s activity, the spatial structure, by assuming equilibrium conditions or, more recently, a probabilistic dynamics with a single arbitrary time-step. Here we show that, under this latter assumption, the inference procedure fails to reconstruct the synaptic matrix of a network of integrate-and-fire neurons when the chosen time scale of interaction does not closely match the synaptic delay or when no single time scale for the interaction can be identified; such failure, moreover, exposes a distinctive bias of the inference method that can lead to infer as inhibitory the excitatory synapses with interaction time scales longer than the model’s time-step. We therefore introduce a new two-step method, that first infers through cross-correlation profiles the delay-structure of the network and then reconstructs the synaptic matrix, and successfully test it on networks with different topologies and in different activity regimes. Although step one is able to accurately recover the delay-structure of the network, thus getting rid of any a priori guess about the time scales of the interaction, the inference method introduces nonetheless an arbitrary time scale, the time-bin dt used to binarize the spike trains. We therefore analytically and numerically study how the choice of dt affects the inference in our network model, finding that the relationship between the inferred couplings and the real synaptic efficacies, albeit being quadratic in both cases, depends critically on dt for the excitatory synapses only, whilst being basically independent of it for the inhibitory ones.

Suggested Citation

  • Cristiano Capone & Carla Filosa & Guido Gigante & Federico Ricci-Tersenghi & Paolo Del Giudice, 2015. "Inferring Synaptic Structure in Presence of Neural Interaction Time Scales," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
  • Handle: RePEc:plo:pone00:0118412
    DOI: 10.1371/journal.pone.0118412
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    References listed on IDEAS

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    1. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    2. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
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    Cited by:

    1. Gabriel Baglietto & Guido Gigante & Paolo Del Giudice, 2017. "Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-25, April.

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