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.
RECENT AND SELECTED PUBLICATIONS
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Valle G., Mazzoni A., Iberite F., D’Anna E., Strauss I., Granata G., Controzzi M., Clemente F., Rognini G., Cipriani C., Stieglitz T., Petrini FM., Rossini PM., Micera S., Biomimetic Intraneural Sensory Feedback Enhances Sensation Naturalness, Tactile Sensitivity, and Manual Dexterity in a Bidirectional Prosthesis, NEURON 37-45 1 :100 (2018);
Mazzoni A., Rosa M., Carpaneto J., Romito LM., Priori P., Micera S., Subthalamic neural activity patterns anticipate economic risk decisions in gambling, eNeuro. 0366-17.2017 (2018).