WEBINAR 6 on “Artificial Sensors: towards human-like sense of touch by means of neuromorphic approach”

Conventional electronic (e-) skin, as a large-area electronic device for sensing tactile events, comprises a sensor array, which analog signal is readout by serially sampling individual sensors in the array.

The generated frame-based data are usually utilized by machine learning based on artificial neural network (ANN) for artificial sense of touch, e.g., object classification, by touching and grasping.

The conventional e-skin has poor energy efficiency that prevents it from up-scalability, and is ill-suited for taking advantage of dynamic tactile information required for rapid tactile feedback.

In this talk, design and development of new e-skins for energy-efficient, dynamic tactile feedback are to be presented.

By means of the neuromorphic approach based on neuroscientific theory, building blocks for artificial tactile sensory systems are designed.

By means of in mixed hardware and software implementation, event-driven neuromorphic tactile systems that efficiently code dynamic tactile information for rapid object classification, and exhibit an enhancement in the spatial resolution in response to touching and grasping events are demonstrated.

WEBINAR 6 – Speaker Biography and Summary

Presentation by Dr. Zhibin Zhang and Dr. James Goodman.

WEBINAR 5 on “Machine Learning – Current Challenges and Opportunities for Neuroprosthetics“

Brain-computer interfaces (BCIs) promise to be an alternative treatment for individuals with a disability, giving them a chance to regain their interaction capacity with the environment and with others. At the same time, Modern AI/ML techniques are changing paradigms in technology and in medicine. 

In combination, BCIs and modern ML have the potential of revolutionizing the treatment of neurological conditions. Yet, when both technologies meet, there are still challenges to face. From decoding the mysteries of brain activity to the development of training tools, steps remain to be taken to harness the full power of AI and include the vibrant ML community in the creation of next-generation BCIs.  

In this 5th webinar, we will talk about the challenges, and thus consequently, the opportunities in current intracortical BCI development. Departing from our own experience decoding brain activity, data science, and high-performance computing, as well as an example BCI study in human subjects, we will talk about what we believe ML can offer to advance BCIs. With this webinar, we also hope to provide some pointers on how some of you, as individuals with programming skills, can help bridge this gap. 

WEBINAR 5 – Speaker Biography and Summary

Presentation by Paolo Viviani (PhD), Dr. Andres Agudelo-Toro and Dr. Brian Dekleva (PhD).