Model fitting page:

Nine model parameters are optimized to reproduce a set () of following features () of spike patterns:

- first-spike latency (fsl)

- post-spike silence (pss)

- spike frequency adaptation (sfa)

- number of inter-spike intervals (n_ISIs)

- burst width (bw)

- post-burst interval (pbi)

- number of bursts (n_bursts)

 

Pattern error in the model is calculated as follows:

,

 

where and are experimentally observed and simulated features respectively. is a feature-specific weight determined by comparing the spike pattern classes of experimental and candidate model traces during the optimization.

 

For more details and discussion of the error function, please see the following article: Venkadesh S, Komendantov AO, Listopad S, Scott EO, De Jong K, Krichmar JL, Ascoli GA. (2018). Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Frontiers in neuroinformatics. https://doi.org/10.3389/fninf.2018.00008

 

For details of firing pattern classification, please see the following article: Komendantov AO, Venkadesh S, Rees CL, Wheeler DW, Hamilton DJ, and Ascoli GA. (2018). Quantitative firing pattern phenotyping of hippocampal neuron types. bioRxiv doi: 10.1101/212084.

 

Optimization code is available here: https://github.com/Hippocampome-Org/Time