Topic: brain-machine interfaces

The Gonzalez Lab at UCSF is recruiting a systems-neuroscience postdoctoral scientist to study how neurons process information and control complex motor behavior. Projects integrate multiphoton and Bessel-beam imaging, electrophysiology, closed-loop patterned stimulation, and quantitative behavior across questions in learning, sleep, sensory feedback, stroke, and brain-machine interfaces.

Visual restoration is certainly the greatest challenge for brain-machine interfaces with the high pixel number and high refreshing rate. In the recent year, we brought retinal prostheses and optogenetic therapy up to successful clinical trials. Concerning visual restoration at the cortical level, prostheses have shown efficacy for limited periods of time and limited pixel numbers. We are investigating the potential of sonogenetics to develop a non-contact brain machine interface allowing long-lasting activation of the visual cortex. The presentation will introduce our genetic-based brain machi

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of

Invasive cortical brain-machine interfaces (BMIs) can significantly improve life quality for motor-impaired patients. However, externally mounted BMIs carry the risk of wound infection, necessitating fully implanted systems with integrated signal processing. Such systems must ensure reliable and accurate data processing while meeting tight latency and energy constraints. To meet these processing requirements, spiking neural networks (SNNs) seem an attractive solution due to their minimal preprocessing requirements of cortical spike trains (CSTs) and their suitability for ultra-low-power, low-