Inferring synaptic structure in presence of neural interaction time scales

PLoS One. 2015 Mar 25;10(3):e0118412. doi: 10.1371/journal.pone.0118412. eCollection 2015.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology
  • Computer Simulation*
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neural Inhibition / physiology
  • Neurons / physiology*
  • Synapses / physiology*

Grants and funding

This research has received financial support from the Italian Research Minister through the FIRB Project No. RBFR086NN1 (http://hubmiur.pubblica.istruzione.it/web/ricerca/home), the European Research Council (ERC) through grant agreement No. 247328, the Italian National Institute for Nuclear Research through the Project Discosynp (http://www.infn.it/index.php?option=com_content&view=article&id=266&Itemid=145&lang=it) and from the EU Project Coronet (http://www.coronet-project.eu/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author GG is affiliated with Mperience srl., but this does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. Mperience srl provided support in the form of salaries for authors [GG], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.