Metamodelling of a two-population spiking neural network.

ABSTRACT: In computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated by comparison against experimental data. The model outputs of neural network models depend on both neuron parameters, connectivity parameters and other model inputs. Successful model fitting requires sufficient exploration of the model parameter space, which can be computationally demanding. Additionally, identifying degeneracy in the parameters, i.e. different combinations of parameter values that produce similar outputs, is of interest, as they define the subset of parameter values consistent with the data. In this computational study, we ap

SUBMITTER: Skaar JW 

PROVIDER: S-EPMC10688753 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature