Publications

1. Dura-Bernal, S. et al. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons. THE JOURNAL OF NEUROSCIENCE 44, (2024) doi:10.1523/jneurosci.1236-24.2024.

2. Zendrikov, D., Solinas, S. & Indiveri, G. Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems. NEUROMORPHIC COMPUTING AND ENGINEERING 3, (2023) doi:10.1088/2634-4386/ace64c.

3. Marasco, A. et al. An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons. BULLETIN OF MATHEMATICAL BIOLOGY 85, (2023) doi:10.1007/s11538-023-01206-8.

4. Gandolfi, D. et al. Full-scale scaffold model of the human hippocampus CA1 area. NATURE COMPUTATIONAL SCIENCE 3, 264–276 (2023) doi:10.1038/s43588-023-00417-2.

5. Gandolfi, D. et al. A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits. SCIENTIFIC REPORTS 12, (2022) doi:10.1038/s41598-022-18024-y.

6. Buchel, J., Zendrikov, D., Solinas, S., Indiveri, G. & Muir, D. R. Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors. SCIENTIFIC REPORTS 11, (2021) doi:10.1038/s41598-021-02779-x.

7. Risi, N., Aimar, A., Donati, E., Solinas, S. & Indiveri, G. A Spike-Based Neuromorphic Architecture of Stereo Vision. FRONTIERS IN NEUROROBOTICS 14, (2020) doi:10.3389/fnbot.2020.568283.

8. Park, D. S. et al. Ionic Mechanisms of Impulse Propagation Failure in the FHF2-Deficient Heart. CIRCULATION RESEARCH 127, 1536–1548 (2020) doi:10.1161/CIRCRESAHA.120.317349.

9. Thiele, J. C., Bichler, O., Dupret, A., Solinas, S. & Indiveri, G. A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation. in Neural Networks (IJCNN), 2019 International Joint Conference on 1–8 (2019). doi:10.1109/IJCNN.2019.8852360.

10. Solinas, S. M. G., Edelmann, E., Lessmann, V. & Migliore, M. A kinetic model for brain-derived neurotrophic factor mediated spike timingdependent LTP. PLOS COMPUTATIONAL BIOLOGY 15, (2019) doi:10.1371/journal.pcbi.1006975.

11. Park, D. S. et al. Fhf2 gene deletion causes temperature-sensitive cardiac conduction failure. NATURE COMMUNICATIONS 7, (2016) doi:10.1038/ncomms12966.

12. Dover, K. et al. FHF-independent conduction of action potentials along the leak-resistant cerebellar granule cell axon. NATURE COMMUNICATIONS 7, (2016) doi:10.1038/ncomms12895.

13. Cattani, A., Solinas, S. & Canuto, C. A hybrid model for the computationally-efficient simulation of the cerebellar granular layer. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 10, (2016) doi:10.3389/fncom.2016.00030.

14. Masoli, S., Solinas, S. & D’Angelo, E. Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization. FRONTIERS IN CELLULAR NEUROSCIENCE 9, (2015) doi:10.3389/fncel.2015.00047.

15. Subramaniyam, S. et al. Computational modeling predicts the ionic mechanism of late-onset responses in unipolar brush cells. FRONTIERS IN CELLULAR NEUROSCIENCE 8, 1–19 (2014) doi:10.3389/fncel.2014.00237.

16. Rossert, C., Solinas, S. M. G., D’Angelo, E., Dean, P. & Porrill, J. Model cerebellar granule cells can faithfully transmit modulated firing rate signals. FRONTIERS IN CELLULAR NEUROSCIENCE 8, (2014) doi:10.3389/fncel.2014.00304.

17. Mapelli, L., Solinas, S. M. G. & D’Angelo, E. Integration and regulation of glomerular inhibition in the cerebellar granular layer circuit. FRONTIERS IN CELLULAR NEUROSCIENCE 8, (2014) doi:10.3389/fncel.2014.00055.

18. Solinas, S., Colnaghi, T. & D’Angelo, E. Ensemble neuronal responses in a large-scale realistic model of the cerebellar cortex. BMC NEUROSCIENCE 14, (2013) doi:10.1186/1471-2202-14-S1-P82.

19. Gandolfi, D., Lombardo, P., Mapelli, J., Solinas, S. M. G. & D’Angelo, E. Theta-frequency resonance at the cerebellum input stage improves spike timing on the millisecond time-scale. FRONTIERS IN NEURAL CIRCUITS 7, (2013) doi:10.3389/fncir.2013.00064.

20. D’Angelo, E. et al. The cerebellar Golgi cell and spatiotemporal organization of granular layer activity. FRONTIERS IN NEURAL CIRCUITS 7, (2013) doi:10.3389/fncir.2013.00093.

21. D’Angelo, E. et al. The cerebellar network from structure to function and dynamics. BRAIN RESEARCH REVIEWS 66, 5–15 (2011) doi:10.1016/j.brainresrev.2010.10.002.

22. Solinas, S., Nieus, T. & D’Angelo, E. A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties. FRONTIERS IN CELLULAR NEUROSCIENCE 14, 4–12 (2010) doi:10.3389/fncel.2010.00012.

23. Dover, K., Solinas, S., D’Angelo, E. & Goldfarb, M. Long-term inactivation particle for voltage-gated sodium channels. THE JOURNAL OF PHYSIOLOGY 588, 3695–3711 (2010) doi:10.1113/jphysiol.2010.192559.

24. D’Angelo, E. U. et al. Timing in the cerebellum: oscillations and resonance in the granular layer. NEUROSCIENCE 162, 805–815 (2009) doi:10.1016/j.neuroscience.2009.01.048.

25. Solinas, S. M. G. et al. Fast-reset of pacemaking and theta-frequency resonance patterns in cerebellar Golgi cells: simulations of their impact in vivo. FRONTIERS IN CELLULAR NEUROSCIENCE 1, 1–9 (2007) doi:10.3389/neuro.03.004.2007.

26. Solinas, S. M. G. et al. Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. FRONTIERS IN CELLULAR NEUROSCIENCE 1, 1–9 (2007) doi:10.3389/neuro.03.002.2007.

27. Solinas, S., REINOUD MAEX & and DE SCHUTTER, E. Dendritic amplification of inhibitory postsynaptic potentials in a model Purkinje cell. EUROPEAN JOURNAL OF NEUROSCIENCE 23, 1207–1218 (2006) doi:10.1111/j.1460-9568.2005.04564.x.

28. Solinas, S. & Hertz, J. Stability of asynchronous firing states in networks with synaptic adaptation. NEUROCOMPUTING 38–40, 915–920 (2001) doi:10.1016/S0925-2312(01)00425-8.