Brain-machine interfaces

…sleep, sensory feedback, stroke, and brain-machine interfaces.

The use of electronic circuits to model neural systems goes back to C. Mead and is present in models, from leaky-integrate-and-fire to Hodking-Huxley. Simulating neural network with analog hardware is attractive: it allows to implement neurocomputations in real time without discretization approximations, it has perfect simulation-time scaling with system size, and it provides ready-to-deploy neuromorphic circuit for applications. There are implementations in CMOS technology, however, they are complex, require sophisticated fabrication facilities and, most important, suffer from significant d

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-

Brain machine interfaces (BMIs) are an important technology for restoring mobility in patients with paralysis and a promising tool for understanding the neural correlates of learning. BMIs have been implemented in mice to study decoding and adaptation during volitional activation of single neurons - yet little is known about how learning restructures the broad networks involved. Here we used 2-photon (2P) imaging over eight days as mice learned to volitionally activate pairs of neurons in the motor cortex (MC) or hippocampus CA3 to receive water rewards. We developed an open source pipeline th

Recent studies have demonstrated the capabilities of rodents for manipulation of a joystick to control the movements of a sipper tube or receive rewards based on displacements of a manipulandum. However, a behavioral paradigm where the rats perceive the changes in the motion direction of a cursor on PC monitor has not been introduced yet. In this work, we present a novel behavioral paradigm that allows rats to control a cursor on a PC monitor by operating levers for reaching targets. The control of the cursor is performed in one-dimensional space depending on the limited cognitive and visual c

In the cerebral cortex, activity dynamics measured at a mesoscopic (0.1 to 1 mm) scale are characterized by both spontaneous and task-related dynamical waves of synchronized neuronal activity ranging from spreading stationary depolarizations (blobs) to traveling waves that can follow complex trajectories throughout the cortical network. These waves are thought to participate in information propagation and processing, but remain poorly understood. To test whether cortical waves can be actively generated and their trajectory controlled, we have implemented a goal-directed task for head-fixed mic

The nervous system is fundamentally a closed loop control device: the output of actions continually influences the internal state and subsequent actions. This is true at the single cell and even the molecular level, where “actions” take the form of signals that are fed back to achieve a variety of functions, including homeostasis, excitability and various kinds of multistability that allow switching and storage of memory. It is also true at the behavioural level, where an animal’s motor actions directly influence sensory input on short timescales, and higher level information about goals and i

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) or neuroprosthetics allow neural control over assistive devices. They also provide an important framework for studying neural plasticity. It has been suggested that learning is essential for robust neuroprosthetic control. However, little is known about neural processes in subcortical regions that support learning a neuroprosthetic skill. Recently, cortico-striatal interactions during neuroprosthetic learning have been explored5 but what are the emergent cerebellar dynamics with cortical neuroprosthetic control is unknown. We performed simultaneous electrophysio

…and can improve performance of brain-machine interfaces (BMIs). But developing a multiscale learning algorithm is challenging because spikes are discrete-valued signals while field potentials are continuous-valued signals with slower time-scales than spikes. Recently, a multiscale learning method based on expectation maximization was developed (multiscale-EM) for multimodal discrete-continuous data, whi

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