A neuron is a specialized cell that uses differences in ions concentration, at both sides of the cellular membrane, to generate and transmit information through electrical signals called “action potentials”. Their morphological properties, dendrites, soma and axon, are all involved in this process and changing their distribution, in conjunction with ionic channels and synapses, can greatly affect the overall activity of a neuron. Most of these properties are well known in mouse and only sparsely in human neurons but allowed the definition of mathematical theories and computational tools to reconstruct and simulate the majority of their electrophysiological behaviours. Compared to simpler modelling approach such as integrate and fire, a multi compartmental model, requires a morphological reconstruction of the neuron, the quantification of the membrane passive properties and the creation/usage of highly specific ionic channels and synapses. The NEURON simulation environment allows all of the previous activity and it provides a set of tools to define each part of the single neuron model. The Python script language is used to interface with NEURON for the model construction and simulations. Also, to leverage external modules to optimize and analyse the results.
A PhD student involved in this research topic will have the opportunity to learn how to build and simulate a single neuron model. From the morphologies, to ionic channels and synapses (MOD files) and the Python/NEURON languages to tie everything together. Computational biology background is preferred.
| Cerebellar multi compartmental models
The models represented in the figure were built based on experimental data to study intrinsic and synaptic properties of specific cerebellar neurons. The Purkinje cell is the most complex neuron of the central nervous system with its extensive dendritic tree. The Golgi cell acts like a pyramidal neuron and show basal and apical dendrites with different synaptic activity. The granule cell is a morphological simple neuron but it showed a wide range of intrinsic and synaptic properties not taken in consideration during previous experimental and modelling approaches. The stellate cell, one of the molecular layer interneurons, fine tune the Purkinje cell output. |
References
- 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. https://doi.org/10.3389/fncel.2015.00047
- Masoli, S., & D’Angelo, E. (2017). Synaptic Activation of a Detailed Purkinje Cell Model Predicts Voltage-Dependent Control of Burst-Pause Responses in Active Dendrites. Frontiers in Cellular Neuroscience, 11(September), 1–18. https://doi.org/10.3389/fncel.2017.00278
- Rizza, M. F., Locatelli, F., Masoli, S., Sánchez-Ponce, D., Muñoz, A., Prestori, F., & D’Angelo, E. (2021). Stellate cell computational modeling predicts signal filtering in the molecular layer circuit of cerebellum. Scientific Reports, 11(1), 3873. https://doi.org/10.1038/s41598-021-83209-w
- Masoli, S., Ottaviani, A., Casali, S., & D’Angelo, E. (2020). Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity. PLoS Computational Biology, 16(12 December), 1–27. https://doi.org/10.1371/journal.pcbi.1007937
- Masoli, S., Sanchez-Ponce, D., Vrieler, N., Abu-Haya, K., Lerner, V., Shahar, T., Nedelescu, H., Rizza, M. F., Benavides-Piccione, R., DeFelipe, J., Yarom, Y., Munoz, A., & D’Angelo, E. (2024). Human Purkinje cells outperform mouse Purkinje cells in dendritic complexity and computational capacity. Communications Biology, 7(1), 5. https://doi.org/10.1038/s42003-023-05689-y