The detailed data-driven computational models can be re-tuned to include pathology-specific neural alterations, in order to shed light on the consequent network dynamics. Given the “scaffold” design of the informatic framework, canonical or any specific neurons and mechanisms can be plugged-in to address ontogenesis, species differences and pathologies. Indeed, the research not only advances our understanding of healthy brain function but also offers a promising avenue for investigating pathological mechanisms and potential therapies. Several configurations can be defined and simulated. For instance, we are modeling the autistic alterations of the mouse cerebellar cortical microcircuit by making modifications to a canonical multicompartmental model of cerebellar granule cell. Other pathological models are developed using point-neuron networks of cerebellar regions, such as dystonia and ataxia models.
A PhD student involved in this research topic will have the opportunity to learn informatic programming in computational neuroscience, to reconstruct and simulate neural models, and to design how to import pathological features and/or treatment-effects into computational models.
| Predictions about impact of detailed lesions at network level
(A) Cerebellar granule cell model in control and autistic knock-out (KO) state: input-output relationship and synaptic currents (B) PSTH (peristimulus time histograms) of each population in the cerebellar microcircuit models |
References
- Geminiani A, Mockevičius A, D’Angelo E, Casellato C. Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model. FRONT. SYST. NEUROSCI. 2022; doi: 10.3389/fnsys.2022.919761
- Geminiani A, Casellato C, Antonietti A, D’Angelo E, and Pedrocchi A. A multiple-plasticity spiking neural network embedded in a closed-loop control system to model cerebellar pathologies. INT J NEURAL SYST 2018; 28; doi: 10.1142/S0129065717500174