Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)

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Accession:235138
"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks."
Reference:
1 . Van Pottelbergh T, Drion G, Sepulchre R (2018) Robust Modulation of Integrate-and-Fire Models. Neural Comput 30:987-1011 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Abstract Izhikevich neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Abstract integrate-and-fire neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian; Brian 2;
Model Concept(s): Bifurcation; Bursting; Action Potential Initiation; Delay; Multiscale; Neuromodulation;
Implementer(s): Van Pottelbergh, Tomas [tmjv2 at cam.ac.uk]; 
Robust Modulation of Integrate-and-Fire Models
Tomas Van Pottelbergh, Guillaume Drion and Rodolphe Sepulchre
Neural Computation 2018 30:4, 987-1011

All files are made to be compatible with Brian2.

Figure 5:
- Izhikevich_bistability.py
- MQIF_bistability.py

Figure 6:
- Izhikevich_spike_latency.py
- MQIF_spike_latency.py

Figure 7:
- Izhikevich_ADP.py
- MQIF_ADP.py

Figure 9:
- MQIF_parabolic_bursting.py

Figure 11:
- MQIF_bursting_modulation.py

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