Uncertainty in Biology

Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 427-447


Neuroswarm: A Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons

  • David Gomez-CabreroAffiliated withUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska InstitutetInstitut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS) Email author 
  • , Salva ArdidAffiliated withInstitut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS)Center for Computational Neuroscience and Neural Technology (CompNet), Department of Mathematics and Statistics, Boston University
  • , Maria Cano-ColinoAffiliated withInstitut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS)Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown
  • , Jesper TegnérAffiliated withUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet
  • , Albert CompteAffiliated withInstitut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS)

* Final gross prices may vary according to local VAT.

Get Access


Candidate mechanisms of brain function can potentially be identified using biologically detailed computational models. A critical question that arises from the construction and analysis of such models is whether a particular set of parameters is unique or whether multiple different solutions exist, each capable of reproducing some relevant phenomenology. Addressing this issue is difficult, and systematic procedures have been proposed only recently, targeting small systems such as single neurons or small neural circuits [16] (Marder and Taylor, Nat Neurosci 14:133–138, 2011), [1] (Achard and De Schutter, PLoS Comput Biol 2:e94, 2006). However, how to develop a methodology to address the problem of non-uniqueness of parameters in large-scale biological networks is yet to be developed. Here, we describe a computational strategy to explicitly approach this issue on large-scale neural network models, which has been successfully applied to computational models of working memory (WM) and selective attention [2] (Ardid, J Neurosci Off J Soc Neurosci 30:2856–2870, 2010), [3] (Cano-Colino et al., Cereb Cortex 24:2449–2463, 2014). To illustrate the approach, we show in this chapter how our strategy applies to the problem of identifying different mechanisms underlying visuospatial WM. We use a well-established biological neural circuit model in the literature [6] (Compte et al., Cereb. Cortex 10:910–923, 2000) as a reference point, which we then perturb by using the Swarm Optimization Algorithm. This algorithm explores the space of biologically unconstrained parameters in the model under the constraint of preserving a solution defined here as a network in which the activity of model neurons mimics the properties of neurons in the dorsolateral prefrontal cortex (dlPFC) of monkeys performing a visuospatial WM task [7] (Funahashi et al., J Neurophysiol 61:331–349, 1989). The results are: (1) identification of a set of model solutions, composed of alternative and, in principle, feasible and sufficient mechanisms generating WM function in a cortical network. In particular, we found that the dynamics of interneurons play a main role in distinguishing among potential circuit candidates. Secondly we uncovered compensatory mechanisms in a subset of the parameters in the model. In essence, the compensatory mechanisms we observe in the different solutions are based on correlations between sets of parameters that shift the local Excitatory/Inhibitory balance in opposite directions. In summary, our approach is able to identify distinct mechanisms underlying a same function, as well as to propose a dynamic solution to the problem of fine-tuning. Our results from the proposed workflow would be strengthened by additional biological experiments aimed to refine the validity of the results.


Prefrontal cortex Workflow Ensemble analysis Working memory model Neuroscience Computational biology