Biorobotics

TRANSLATIONAL NEURAL ENGINEERING AREA

Computational Neuroengineering Laboratory

The Computational Neuroengineering Laboratory studies information processing in the nervous system. 
We combine methods from Computational Neuroscience – decoding and information analysis of biological and artificial neural data, spiking neuronal networks simulations – with the application-driven approach of Biomedical Engineering and Neuro Robotics. Understanding information processing is indeed a key feature for the development of neural interfaces, and such interfaces can in turn be used to validate neural models. Moreover, capturing neural coding dynamics is a basic step toward the development of biomimetic software/hardware for data processing.
Theoretical studies on the origin of neural signals, information transmission, and dynamics of neuronal networks are then complemented by a broad range of Biorobotic applications, spanning from invertebrates to humans, from sensory processing to decision making.

As examples of such applications, we are working on the analysis of healthy and pathological neural dynamics in the autonomic nervous system and in the basal ganglia to shed light on metabolic and neurodegenerative diseases, and we are contributing with the analysis of behavioral and neural responses to external stimuli to the development of novel upper and lower limb neuroprostheses. Recent modeling works include instead:  sleep/wake transition in thalamic networks, synaptic and network factors determining the local field potential, and phase-of-firing code in single neurons.

 

Principal Investigator:

Dott. Alberto Mazzoni

 

RECENT PUBLICATIONS

Network models

Stellino F., Mazzoni A., Storace M., Phase analysis methods for burst onset prediction, Physical Review E 95 (2), 022412 (2017);
Barardi A, Garcia-Ojalvo J, Mazzoni A.,Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus, PLoS ONE 11(9) e0161934 (2016);
Mazzoni A., Lindén H., Cuntz H., Lansner A., Panzeri S., Einevoll GT., Computing the Local Field Potential (LFP) from Integrate-and-Fire network models, PLoS Comp Biol 11 e1004584 (2015).
 

Analysis of Sensory Information

Oddo CM., Mazzoni A., Spanne A., Enander JMD., Micera S., Jorntell H., Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons, Scientific Reports 8, 45898 (2017);
Oddo CM., Raspospovic S., Artoni F., Mazzoni A., Carrozza MC., Guglielmelli E., Rossini PM., Faraguna U., Micera S., Intraneural discrimination of textural features by artificial fingertip in intact and amputee humans, eLife 5, e09148 (2016);
Rongala UB., Mazzoni A., Oddo CM., Neuromorphic Artificial Touch for Categorization of Naturalistic Textures, IEEE TNNLS (2015).

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