Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

Working memory is the sketchpad of consciousness, the fundamental mechanism the brain uses to gain volitional control over its thoughts and actions. For the past 50 years, working memory has been thought to rely on cortical neurons that fire continuous impulses that keep thoughts “online”. However, new work from our lab has revealed more complex dynamics. The impulses fire sparsely and interact with brain rhythms of different frequencies. Higher frequency gamma (>35 Hz) rhythms help carry the contents of working memory while lower frequency alpha/beta (~8-30 Hz) rhythms act as control signals that gate access to and clear out working memory. In other words, a rhythmic dance between brain rhythms may underlie your ability to control your own thoughts.

Constraints on control-dependent processing have become a fundamental concept in general theories of cognition that explain human behavior in terms of rational adaptations to these constraints. However, theories miss a rationale for why such constraints would exist in the first place. Recent work suggests that constraints on the allocation of control facilitate flexible task switching at the expense of the stability needed to support goal-directed behavior in face of distraction. We formulate this problem in a dynamical system, in which control signals are represented as attractors and in which constraints on control allocation limit the depth of these attractors. We derive formal expressions of the stability-flexibility tradeoff, showing that constraints on control allocation improve cognitive flexibility but impair cognitive stability. We provide evidence that human participants adapt higher constraints on the allocation of control as the demand for flexibility increases but that participants deviate from optimal constraints. In continuing work, we are investigating how collaborative performance of a group of individuals can benefit from individual differences defined in terms of balance between cognitive stability and flexibility.

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

Working memory is the sketchpad of consciousness, the fundamental mechanism the brain uses to gain volitional control over its thoughts and actions. For the past 50 years, working memory has been thought to rely on cortical neurons that fire continuous impulses that keep thoughts “online”. However, new work from our lab has revealed more complex dynamics. The impulses fire sparsely and interact with brain rhythms of different frequencies. Higher frequency gamma (> 35 Hz) rhythms help carry the contents of working memory while lower frequency alpha/beta (~8-30 Hz) rhythms act as control signals that gate access to and clear out working memory. In other words, a rhythmic dance between brain rhythms may underlie your ability to control your own thoughts.