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Alessandro Vato Write a Message

Researcher
Researcher

Research Line

Neural Computation

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Corso Bettini 31, 38068 Rovereto (TN)
+39 010 71781 470

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About

Alessandro Vato received the M.Eng. degree in electronics engineering and the Ph.D. degree in Bioengineering and Bioelectronics both from the University of Genova, Genova, Italy in 2000 and 2004, respectively.

His long-term goal is to develop novel invasive adaptive neuromodulation systems that interact directly with the nervous system to treat important neurological disorders, and to test them in appropriate human patient populations.

He has pursued this vision since the beginning of his research experience. He explored different levels of organization in the nervous system, from networks of in-vitro cultured neurons (as PhD student), to anaesthetized or behaving rodents (as post-doc), to human subjects (current research).

As a PhD student, he focused his research on exploring the computational capability and the plastic processes that take place in random networks of cultured neurons of rat cortex coupled to arrays of planar microelectrodes. Together with his colleagues he characterized the spontaneous and electrically-evoked activity of these neural network and developed a hybrid device by establishing a bidirectional connection between cultured neurons and a mobile robot. This work provided the basis for his PhD thesis (“Connecting neurons to artificial devices: a new tool for investigating the neural code”) and his PhD degree in Bioengineering and Bioelectronics.

As postdoctoral fellow at Northwestern University, he contributed to the NIH-funded project “Development of a bidirectional brain-machine interface.” He learned the surgical procedures to chronically implant multi-electrode arrays in rat cortex, the behavioral techniques to train them on sensory-motor tasks, and how to combine electrophysiological recordings with intracortical microstimulation. He was interested in using intracortical micro-stimulation patterns in behaving rats to build an artificial sensory input channel. He explored which stimulation parameters are needed to create an artificial sensation and how to encode information coming from the environment into an electrical signal that can be felt and understood by the subject.

After his postdoctoral training, he set up his own lab at the Italian Institute of Technology. There, he developed a bidirectional neural interface inspired by the operation of the vertebrate spinal cord as the prime biological interface between the brain and the musculoskeletal apparatus. He designed a method to decode force information from motor cortical activity, and to translate this information into patterns of electrical stimuli that were delivered to the somatosensory cortex of anesthetized rats. Based on my initial work in this area, he received an EU-funded grant entitled “SiCode: Towards new BMIs: State dependent information coding” with the goal of understanding the state dependency of neuronal responses to external stimuli and to use this knowledge to improve bi-directional communication between brains and machines. In the context of this work, he also developed a programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals and a modular bidirectional neural interface by using an ultra-low-power neuromorphic.

Projects

How to optimally extract relevant information from recorded neural activity, and how to improve the efficacy of current brain stimulation techniques are crucial questions for both basic and applied neuroscience. Thus, addressing these questions is important for understanding how the brain processes information as well as for the development of neural interfaces that directly interact with the nervous system to treat neurological disorders.

1. Develop novel invasive adaptive neuromodulation systems that interact directly with the nervous system

In order to improve the performance of a neural interface, the stimulation parameters need to be adapted in real time according to the current state of the brain. This is supported by the observation that neural responses to a sensory stimulus do not only depend on extrinsic sensory inputs, but also on intrinsic network variables that can be collectively defined as the neural state. The principles that govern these interactions between spontaneous activity, the external stimulation, and the internal state of the brain are still largely unknown.

The goal of my research over the upcoming years is to increase our knowledge about the dynamics of the brain processes with particular attention to understanding: a) how to extract useful information from different frequencies of the recorded neural signals; b) how the brain routes the information flow across different cortical regions; and finally c) how the responses of the brain to external stimuli are affected by the internal neural state changes. My final goal is to use such knowledge in designing new adaptive closed-loop neuromodulation devices for the treatment of neurological disorders such as epilepsy or Parkinson’s disease (PD).

2. Develop of a bidirectional Brain Machine Interface

The main idea of a Brain Machine Interface system consists in extracting neural signals directly from the brain and use them to control external devices. In the framework of building neural prostheses this technique could be useful to better understand how the brain processes the sensory information coming from the environment and uses it to build motor commands. To reach this result a crucial point is to develop a BMI real-time system to create a bidirectional communication channel with the nervous system.

In our lab we are implementing in-vivo techniques using multielectrode microwires arrays chronically implanted in the cortex of awake rodents. These techniques permit to record the neural activity from the cortex while the animal is behaving and simultaneously to deliver Intracortical Micro Stimulation (ICMS) patterns providing an artificial input in a closed-loop system.

In this research topics we are developing a novel bidirectional neural interface emulating the functionalities of the spinal cord. In vertebrates the spinal cord mediates the communications between brain and limb mechanics, combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We embedded a portion of the central nervous system within a closed-loop system controlling the movements of a point mass, whose behavior emerges from the combined dynamical properties of its neural and artificial components. Our system included (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generated a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. The obtained results open new perspectives within the possibility of closing the sensory-motor loop to restore a connection with the world for people with severe paralysis.

3- Intracortical Micro Stimulation as artificial sensory channel

This research theme has been explored by designing two experiments both involving behaving rats with a microwires array chronically implanted in the barrel cortex. In the first experiment we took inspiration from a well-known behavioral paradigm called "gap crossing" and we trained the rats in a dark room to jump between two platforms after inferring the distance of the second platform by information collected by the whiskers. The goal is to provide the same distance-information by using the intracortical microstimulation of the barrel cortex and substituting the natural sensation with an artificial one.

In the second experiment we explored the effects of multimodal stimulations to be used as artificial feedback for a bidirectional BMI system on rats. We focused on the sensory perception investigating which are the best modalities to translate an artificial feedback in a coherent and representative stimulus able to encode information collected form the environment. We developed a novel behavioral paradigm in which behaving rats chronically implanted with an array of microelectrodes are subjected to different multimodal stimulations (audio and intracortical electrical micro-stimulation). Rats are trained to recognize between high-frequency and low-frequency stimulations, using both audio signals and ICMS patterns, by pressing a different lever inside a behavioral box. We used this experimental paradigm to explore which modality is predominant in the case of incongruent stimulation (i.e. hi-freq. audio simultaneous with low-freq. intracortical electrical stimulation) and which is the role of the intracortical microstimulation in the learning process of this multimodal decision making experiment.

Selected Publications

Original Scientific Papers

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. 

Book

  • Vato A. (2015). Arrivano i cyborg. Dove neuroscienze e bioingengeria si incontrano (The cyborgs are coming: neuroscience and bioengineering meet), Italian language, Hoepli Press, p.144.

Book chapters

  • Semprini M., Boi F. and Vato A. (2016). Bidirectional Brain-Machine Interfaces. In: Closed Loop Neuroscience, edited by El Hady, Elsevier.
  • Bonzano L., Vato A., Chiappalone M. and Martinoia S. (2007). Modulation of electrophysiological activity in neural networks: towards a bioartificial living system. In: Handbook of Neural Engineering, edited by Matin Akay, Wiley/IEEE Press, p.29-40.
  • Martinoia S., Chiappalone M. and Vato A. (2004). Bioartificial neuronal networks: coupling networks of biological neuron to microtransducer arrays. In: Smart adaptive system on silicon, edited by Valle M, Boston: Kluwer Academic Publisher, 285-302. 

Dissertations

  • Vato A. (2004). Connecting neurons to artificial devices: a new tool for investigating the neural code. PhD thesis, University of Genova, (Italy).
  • Vato A. (2000). System for the management and processing of neuronal electrophysiological signals. M.Eng. thesis, University of Genova, (Italy).

Peer-review  Proceedings

  • Boi F., Semprini M., Vato A. (2016) A Non-Linear Mapping Algorithm Shaping the Control Policy of a Bidirectional Brain Machine Interface. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando (FL, USA).
  • Semprini M., Boi F., Tucci V., Vato A. (2016) A Study on the Effect of Multisensory Stimulation in Behaving Rats. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando (FL, USA).
  • Boi F., Semprini M., Mussa Ivaldi F.A., Panzeri S. and Vato A. (2015) A bidirectional brain-machine interface connecting alert rodents to a dynamical system. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, pp. 51-54, Milan (Italy).
  • Boi F., Diotalevi F., Stefanini F., Indiveri G., Bartolozzi C. and Vato A. (2015) A modular configurable system for closed-loop bidirectional brain-machine interfaces. International IEEE EMBS Conference on Neural Engineering, NER 2015, pp. 198-201, Montpellier (France).
  • Angotzi G.N., Boi F., Zordan S. and Vato A. (2013). A compact wireless multi-channel system for real-time intracortical microstimulation of behaving rodents. International IEEE/EMBS Conference on Neural Engineering, NER 2013, pp. 1009-1012, San Diego, (CA, USA).
  • Semprini M., Bennicelli L. and Vato A. (2012). A parametric study of intracortical microstimulation in behaving rats for the development of artificial sensory channels. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012, pp. 799-802, San Diego, (CA, USA).
  • Szymanski F.D., Semprini M., Mussa-Ivaldi F. A., Fadiga L., Panzeri S. and Vato A. (2011). Dynamic brain-machine interface: A novel paradigm for bidirectional interaction between brains and dynamical systems. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, pp. 4592-4595, Boston, (MA, USA).
  • Vato A., Bonzano L., Chiappalone M., Novellino A., Tedesco M.B., Bove M. and Martinoia S. (2003) Modulating neural networks dynamics: multi-site electrical stimulation of in-vitro cortical neurons coupled to MEA devices. First International IEEE/EMBS Conference on Neural Engineering, NER 2003, pp. 466-489, Capri Island (Italy).
  • Grattarola M., Chiappalone M., Davide F., Martinoia S., Tedesco M.B., Rosso N. and Vato A. (2001) Burst analysis of chemically stimulated spinal cord neuronal networks cultured on microelectrode arrays. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2001, pp. 792-732, Istanbul (Turkey). 

 

 

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L’Istituto Italiano di Tecnologia (IIT) è una fondazione di diritto privato - cfr. determinazione Corte dei Conti 23/2015 “IIT è una fondazione da inquadrare fra gli organismi di diritto pubblico con la scelta di un modello di organizzazione di diritto privato per rispondere all’esigenza di assicurare procedure più snelle nella selezione non solo nell’ambito nazionale dei collaboratori, scienziati e ricercatori ”.

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Lo staff complessivo di IIT conta circa 1440 persone. L’area scientifica è rappresentata da circa l’85% del personale. Il 45% dei ricercatori proviene dall’estero: di questi, il 29% è costituito da stranieri provenienti da oltre 50 Paesi e il 16% da italiani rientrati. Oggi il personale scientifico è composto da circa 60 principal investigators, circa 110 ricercatori e tecnologi di staff, circa 350 post doc, circa 500 studenti di dottorato e borsisti, circa 130 tecnici. Oltre 330 posti su 1400 creati su fondi esterni. Età media 34 anni. 41% donne / 59 % uomini.

Nel 2015 IIT ha ricevuto finanziamenti pubblici per circa 96 milioni di euro (80% del budget), conseguendo fondi esterni per 22 milioni di euro (20% budget) provenienti da 18 progetti europei17 finanziamenti da istituzioni nazionali e internazionali, circa 60 progetti industriali

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Istituto Italiano di Tecnologia (IIT) is a public research institute that adopts the organizational model of a private law foundation. IIT is overseen by Ministero dell'Istruzione, dell'Università e della Ricerca and Ministero dell'Economia e delle Finanze (the Italian Ministries of Education, Economy and Finance).  The Institute was set up according to Italian law 326/2003 with the objective of promoting excellence in basic and applied research andfostering Italy’s economic development. Construction of the Laboratories started in 2006 and finished in 2009.

IIT has an overall staff of about 1,440 people. The scientific staff covers about 85% of the total. Out of 45% of researchers coming from abroad 29% are foreigners coming from more than 50 countries and 16% are returned Italians. The scientific staff currently consists of approximately 60 Principal Investigators110 researchers and technologists350 post-docs and 500 PhD students and grant holders and 130 technicians. External funding has allowed the creation of more than 330 positions . The average age is 34 and the gender balance proportion  is 41% female against 59% male.

In 2015 IIT received 96 million euros in public funding (accounting for 80% of its budget) and obtained 22 million euros in external funding (accounting for 20% of its budget). External funding comes from 18 European Projects, other 17 national and international competitive projects and approximately 60 industrial projects.

So far IIT accounts for: about 6990 publications, more than 130 European grants and 11 ERC grants, more than 350 patents or patent applications12 up start-ups and as many  which are about to be launched. The Institute’s scientific activity has been further strengthened since 2009 with the establishment of 11 research nodes throughout Italy (Torino, Milano, Trento, Parma, Roma, Pisa, Napoli, Lecce, Ferrara) and abroad (MIT and Harvard University, USA), which, along with the Genoa-based Central Lab, implement the research programs included in the 2015-2017 Strategic Plan.