Implementations of the inference methods for integrate-and-fire circuit models described in:
Ladenbauer et al., Inferring and validating mechanistic models of neural
microcircuits based on spike-train data
Nature Communications 10:4933 (2019) [bioRxiv preprint]
The code contains examples for inference of
- background inputs
- input perturbations
- synaptic coupling
- neuronal adaptation
How to use:
run one of baseline_input_inference.py, input_perturbation_inference.py, network_inference.py,
adaptation_inference.py (tested with Python 2.7 and 3.7)
Each script generates output graphs similar to those of the respective results section in the paper, typical run times are indicated in the scripts
Required Python libraries: numpy, scipy, numba, multiprocessing, math, os, collections, tables, time, matplotlib, warnings
These libraries can be conveniently obtained, for example, via a recent Anaconda distribution
For questions please contact Josef Ladenbauer