| Project Detail |
Using computational feedback to probe visual perception in real time
Visual information is processed continuously, and the nervous system has the capacity to react flexibly to updated sensory input. But these same neuronal mechanisms must also support perceptual stability. The EU-funded COGSTIM project will investigate the functional neuronal networks that support this balance between perceptual flexibility and stability within primate visual areas. The project will combine dense electrophysiological recording with real-time decoding of neuronal correlates of the subject’s perceptual choice, based on adaptive machine learning algorithms. To control visual perception predictably, closed-loop electrical stimulation will be applied under dynamically adjusted feedback. Using joint decoding and stimulation will serve as a basis for the future development of rehabilitative therapeutical protocols as well as innovative brain-machine interfaces suitable for real-world use.
Computational models of vision often address problems that have a single and definite end-point, such as visual recognition: an example of this might be to find a ripe banana in a complex scene. However, not all computation is of this form. Visual information is processed continuously in sensory areas and the nervous system has the capacity to alter or halt an ongoing behavioral response to changes in incoming information. We can therefore react flexibly to updated sensory input or changed requirements for motor output. On the other hand, these same neuronal mechanisms must also support perceptual stability, so that noisy signals do not cause loss of a crucial goal.
In project COGSTIM, I will investigate the functional neuronal networks that support the balance between perceptual flexibility and stability, within primate visual areas. I will use a highly innovative approach, combining dense electrophysiological recording with online (real-time) decoding of neuronal correlates of the subject’s perceptual choice, based on adaptive machine-learning algorithms. In order to control visual perception effectively and predictably, closed-loop electrical stimulation will be applied under dynamically adjusted feedback to identified neuronal circuits that causally modulate associated percepts. Crucially, this novel approach using joint decoding and stimulation in real time will allow me to target dynamically visual percepts, representing a significant advance in our understanding of on-going, continuous computations of the primate brain. Such developments offer promising bases for the future development of rehabilitative therapeutical protocols, as well as innovative brain machine interfaces suitable for real-world use. |
