Papers

Saussus, O. De Schrijver, S., Garcia Ramirez, J., Decramer, T., Janssen, P. Abstract We present an innovative intracortical brain-computer interface (BCI) to bridge the gap between laboratory settings and real-world applications. This BCI approach introduces three key advancements. First, we used neural signals from three macaque brain regions—primary motor, dorsal, and ventral premotor cortex—enabling precise and flexible decoding of real-time three-dimensional (3D) sphere/a

Lozano, A., Chen, X., La Grouw, M., Li, B., Wang, F., van der Grinten, M., Soto-Sánchez, C., Morales-Gregorio, A., Fernández E. and Roelfsema, P.R. Abstract Background High-channel-count neuroprostheses could one day restore functional vision in blind individuals by delivering electrical pulses to electrodes in the visual cortex that elicit perceptions known as ‘phosphenes’. However, if a high number of electrodes are used, it becomes challenging and time-consuming to map the

Feulner, B., Perich, M. G., Miller, L. E., Clopath, C., Gallego, J. A. Abstact Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural ne

Safaie, M., Chang, J. C., Park, J., Miller, L. E., Dudman, J. T., Perich, M. G., Gallego, J. A. Abstact Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization

Gallego, J. A., Makin, T. R., McDougle, S.D. Abstact Brain–computer interfaces (BCIs) for movement restoration typically decode the user’s intent from neural activity in their primary motor cortex (M1) and use this information to enable ‘mental control’ of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related

Andersen, R. A., Aflalo, T., Bashford, L., Bjånes, D., Kellis, S. Abstact Traditional brain–machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuropro

Gallego, J. A., Perich, M. G., Chowhury, R. H., Solla, S. A., Miller, L. E. Abstact Animals readily execute learned behaviors in a consistent manner over long periods of time, and yet no equally stable neural correlate has been demonstrated. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which captures the dominant co-variation pattern