In the Fallingwater house, artificial and natural are bonding together in harmony, creating one of humankind’s most beautiful masterpieces. After more than 80 years, the water still doesn’t give up. Moisture, flooding, leaks are part of the daily relationship between nature and men’s manufacture. To obtain and keep harmony, the artificial continuously needs to respect, listen, and adapt to the natural.
Research in Neural Engineering shares the same goal (harmony), the same issues (e.g.,”leaks” in electrode–tissues interface ), and the same method (respect, listening, adaptation)
Brain Machine Interface
Wireless Recording-Stimulation System
Cultured Neural Networks
Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems.
I focused on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. Bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.
- Boi, F., Moratis, T., De Feo, V., Diotalevi, F., Bartolozzi, C., Indiveri, G., and Vato, A. (2016) A bidirectional brain-machine interface featuring a neuromorphic hardware decoder. Frontiers in Neuroscience, 10,563.
- Vato A., Szymanski F.D., Semprini M., Mussa-Ivaldi F.A. and Panzeri S. (2014). A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields. PLoS One 9(3), e91677.
- Vato A., Semprini M., Maggiolini E., Szymanski F.D., Fadiga L., Panzeri S. and Mussa-Ivaldi F.A. (2012). Shaping the dynamics of a bidirectional neural interface. PLoS Computational Biology 8(7), e1002578.
- Mussa-Ivaldi F.A., Alford S.T., Chiappalone M., Fadiga L., Karniel A., Kositsky M., Maggiolini E., Panzeri S., Sanguineti V., Semprini M. and Vato A. (2010). New perspectives on the dialogue between brains and machines. Frontiers in Neuroscience 4(1), 44-52.
I am an electronic/biomedical engineer and neuroscientist. From the beginning of my research career, I have explored and characterized how to interact with neural circuits at different organizational levels.
I have pursued my research interests by gaining extensive expertise in developing new hardware and software experimental tools focusing on engineering and scientific aspects of closed-loop neuromodulation by designing and running different experimental paradigms and developing several computational algorithms. The latter is critical for processing data to help researchers correctly interpret results.
For example, in one of my earliest work, I explored the mechanisms underlying neural plasticity by delivering electrical stimulation patterns to randomly cultured neural networks coupled to planar microelectrode arrays. Later, I used cortical micro-stimulation to create an artificial sensory channel for a new family of closed-loop bidirectional brain-machine interfaces.
1. De Feo, V., Boi, F., Safaai, H., Onken, A., Panzeri, S., and Vato, A. (2017) State-dependent decoding algorithms improve the performance of a bidirectional BMI in anesthetized rats. Frontiers in Neuroscience, 11, 269.
2. Boi, F., Moratis, T., De Feo, V., Diotalevi, F., Bartolozzi, C., Indiveri, G., and Vato, A. (2016) A bidirectional brain-machine interface featuring a neuromorphic hardware decoder. Frontiers in Neuroscience, 10,563.
3. Panzeri, S., Safaai, H., De Feo, V., and Vato, A. (2016) Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces. Frontiers in Neuroscience, 10, 165.
4. Angotzi G.N., Baranauskas G., Vato A., Bonfanti A., Zambra G., Maggiolini E., Semprini M., Ricci D., Ansaldo A, Castagnola E., Ius T., Skrap M. and Fadiga L. (2015) A Compact and Autoclavable System for Acute Extracellular Neural Recording and Brain Pressure Monitoring for Humans. IEEE Transactions on Biomedical Circuits and Systems 9(1), 50-59.
5. Angotzi G.N., Boi F., Zordan S., Bonfanti A. and Vato A. (2014) A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals. Scientific Reports 4, 5963.
6. Vato A., Szymanski F.D., Semprini M., Mussa-Ivaldi F.A. and Panzeri S. (2014). A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields. PLoS One 9(3), e91677.
7. Vato A., Semprini M., Maggiolini E., Szymanski F.D., Fadiga L., Panzeri S. and Mussa-Ivaldi F.A. (2012). Shaping the dynamics of a bidirectional neural interface. PLoS Computational Biology 8(7), e1002578.
8. Bonfanti A., Ceravolo M., Zambra G., Gusmeroli R., Baranauskas G., Angotzi G.N., Vato A., Maggiolini E., Semprini M., Spinelli A.S. and Lacaita A.L. (2012). A Multi-Channel Low-Power System-on-Chip for in Vivo Recording and Wireless Transmission of Neural Spikes. Journal of Low Power Electronics and Applications 2(4), 211–241.
9. Baranauskas G., Maggiolini E., Vato A., Angotzi G., Bonfanti A., Zambra G., Spinelli A. and Fadiga L. (2012). Origins of 1/f2 scaling in the power spectrum of intracortical local field potential. Journal of Neurophysiology 107(3), 984-994.
10. Baranauskas G., Maggiolini E., Castagnola E., Ansaldo A., Mazzoni A., Angotzi G.N., Vato A., Ricci D., Panzeri S. and Fadiga, L. (2011). Carbon nanotube composite coating of neural microelectrodes preferentially improves the multiunit signal-to-noise ratio. Journal of Neural Engineering 8(6), 066013.
11. Bonfanti A., Zambra G., Baranauskas G., Angotzi G.N., Maggiolini E., Semprini M., Vato A., Fadiga L., Spinelli A.S. and Lacaita, A.L. (2011). A wireless microsystem with digital data compression for neural spike recording. Microelectronic Engineering 88(8), 1672-1675.
12. Mussa-Ivaldi F.A., Alford S.T., Chiappalone M., Fadiga L., Karniel A., Kositsky M., Maggiolini E., Panzeri S., Sanguineti V., Semprini M. and Vato A. (2010). New perspectives on the dialogue between brains and machines. Frontiers in Neuroscience 4(1), 44-52.
13. Chiappalone M., Vato A., Berdondini L., Koudelka-Hep M. and Martinoia S. (2007). Network dynamics and synchronous activity in cultured cortical neurons. International Journal of Neural Systems 17(2), 87-103.
14. Chiappalone M., Bove M., Vato A., Tedesco M. and Martinoia, S. (2006). Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Research 1093(1), 41-53.
15. Chiappalone M., Novellino A., Vajda I., Vato A., Martinoia S. and Van Pelt, J. (2005). Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neurocomputing 65-66, 653-662.
16. Stillo G., Bonzano L., Chiappalone M., Vato A., Davide F.A. and Martinoia S., (2004). Burst on Hurst algorithm for detecting activity patterns in networks of cortical neurons. International Journal of Information Technology 1(4), 135-138.
17. Vato A., Bonzano L., Chiappalone M., Cicero S., Morabito F., Novellino A. and Stillo G. (2004). Spike manager: A new tool for spontaneous and evoked neuronal networks activity characterization. Neurocomputing 58-60, 1153-1161.
18. Novellino A., Chiappalone M., Vato A., Bove M., Tedesco M.B. and Martinoia, S. (2003). Behaviors from an electrically stimulated spinal cord neuronal network cultured on microelectrode arrays. Neurocomputing 52-54, 661-669.
19. Chiappalone M., Vato A., Tedesco M., Marcoli M., Davide F. and Martinoia S. (2003). Networks of neurons coupled to microelectrode arrays: A neuronal sensory system for pharmacological applications. Biosensors and Bioelectronics 18(5), 627-634.