This is the README for Purkinje cell model code for the paper: Masoli, S., Solinas, S., & D’Angelo, E. (2015). Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization. Frontiers in Cellular Neuroscience, 9(February), 1–22. http://doi.org/10.3389/fncel.2015.00047 Implementation done by Stefano Masoli in Python/Neuron. The Purkinje cell (PC) is among the most complex neurons in the brain and plays a critical role for cerebellar functioning. PCs operate as fast pacemakers modulated by synaptic inputs but can switch from simple spikes to complex bursts and, in some conditions, show bistability. In contrast to original works emphasizing dendritic Ca-dependent mechanisms, recent experiments have supported a primary role for axonal Na-dependent processing, which could effectively regulate spike generation and transmission to deep cerebellar nuclei (DCN). In order to account for the numerous ionic mechanisms involved (at present including Nav1.6, Cav2.1, Cav3.1, Cav3.2, Cav3.3, Kv1.1, Kv1.5, Kv3.3, Kv3.4, Kv4.3, KCa1.1, KCa2.2, KCa3.1, Kir2.x, HCN1), we have elaborated a multicompartmental model incorporating available knowledge on localization and gating of PC ionic channels. The axon, including initial segment (AIS) and Ranvier nodes (RNs), proved critical to obtain appropriate pacemaking and firing frequency modulation. Simple spikes initiated in the AIS and protracted discharges were stabilized in the soma through Na-dependent mechanisms, while somato-dendritic Ca channels contributed to sustain pacemaking and to generate complex bursting at high discharge regimes. Bistability occurred only following Na and Ca channel down-regulation. In addition, specific properties in RNs K currents were required to limit spike transmission frequency along the axon. The model showed how organized electroresponsive functions could emerge from the molecular complexity of PCs and showed that the axon is fundamental to complement ionic channel compartmentalization enabling action potential processing and transmission of specific spike patterns to DCN. Requirement: The model was built under Mint (ubuntu based) with NEURON 7.3 and Python 2.7. CHANGELOG - April 2021 The code runs with NEURON 7.8, NEURON 8 and with Python3.6 to 3.8 It requires a powerfull CPU with as many cores as possibile. The model uses NEURON multisplit to distribute automatically the calculation on all the available cores. A typical 5s simulation takes about 15m on an AMD 8350 8 core CPU and less than 4m on an AMD 1800x 8cores/16thread CPU. Usage instructions: Download and extract the archive. Under Linux/Unix: Change directory to "purkinjecell" folder. Run nrnivmodl ./mod_files to compile the mod files. The model is provided with 6 protocols: Run "nrngui -python ./protocols/01_no_channels_ais_py3.py" for the first protocol. The other protocols are numbered from 02 to 06. Here is a screenshot from running the command: nrngui -python protocols/05_calcium_sodium_bursts_py3.py Attention: The model does not work with the variable time step! If you would like more help please refer to: https://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.cshtml NOTES: Not tested under Win installations.