This is the README for the CA1 dopamine model for the published paper:
In silico investigation of the puzzling dopamine effects on excitability and synaptic plasticity in hippocampal CA1 pyramidal neurons
https://doi.org/10.1038/s41598-025-17694-8
Enrico Manara, Andrea Mele & Michele Migliore
It has been shown that in the CA1 region of the hippocampus, dopamine modulates memory functions by influencing spike-timing-dependent plasticity (STDP) and intrinsic neuronal properties. Although experimental findings have suggested potential mechanisms, their detailed interplay remains incompletely understood. Here, using a realistic CA1 pyramidal neuron model, we have investigated the possible effects of dopaminergic modulation on a neuron’s signal integration and synaptic plasticity processes. The results suggest a physiological plausible explanation for the puzzling experimental observation that long-term potentiation (LTP) increases in spite of a reduction in the neuron’s excitability, and explains why physiological dopamine levels are necessary for LTP induction. The model suggests experimentally testable predictions on which ion channel kinetic properties can modulate the interplay between synaptic plasticity and neuronal excitability, thereby identifying potential molecular targets for therapeutic intervention.
Implemented in Jupyter Notebook, using NEURON 9.0.0 with Python language
1. Go to cell 39 to change the model parameters (“Low dopamine [from paper]”, “Normal dopamine [from paper]”, “Possible pharmacological alternative [from paper]”, and “User’s custom”). If values are changed in the Edit parameters box, the Apply custom parameters button must be pressed to save and apply them to the model.
2. Go to cell 40 to run Input/Output simulations for the selected condition, reproducing Fig. 2 and part of Fig. 6 from the paper. The number of runs can be edited as desired. The exact injected current amplitudes can be adjusted in cell 13 (variables initial_amp and increment). For each run, the soma membrane potential trace of that run only is shown (the trace is updated and replaced at every new run).
3. Go to cell 41 to run spike-time-dependent plasticity (STDP) simulations for the selected condition. This reproduces Fig. 3b from the paper and shows the synaptic weight increase for current run. In each run, 10 random synapses are relocated in the stratum radiatum of the neuron. The number of runs can be edited as desired. The results showed, as in the paper, were obtained using 50 runs for each condition.
See the original paper for further details.
Attention:
- The model works best with the variable time step method enabled.
- If, for any reason, the .mod files in the morphology_and_mod/mod folder are not correctly loaded, please recompile them by changing the cell immediately above cell 3 from Raw to Code, then run that cell.
- In the paper, the results were obtained using NEURON 8.2.3, which relies on the Random.ACG() method. The ebrains-25.10-rc kernel, however, uses NEURON 9.0.0, in which Random.ACG() has been removed and replaced by the Random.Random123() method. Consequently, the randomization of synapse locations differs, and the results of the STDP simulations in this Jupyter notebook vary slightly from those reported in the paper.
Questions on how to use this model should be directed to michele.migliore@cnr.it or enrico.manara@ibf.cnr.it